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This presentation discusses hypothesis testing and p-values. It defines a hypothesis as a proposed explanation that can be scientifically tested. The null hypothesis is the initial assumption that is tested, while the alternative hypothesis is its opposite. A study aims to provide evidence for or against the null hypothesis. The p-value estimates the probability that the null hypothesis is true due to chance. If a p-value is less than 0.05, there is less than a 5% probability that the null hypothesis is correct, so it can be rejected. Type I and Type II errors occur when the null hypothesis is incorrectly rejected or accepted.

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Hypothesis testing and p values 06

This document discusses hypothesis testing and p-values. It begins by defining a hypothesis as a proposition or prediction about the outcome of an experiment. Hypotheses are formulated and tested through science to evaluate their credibility. There are two main types of hypotheses: the null hypothesis, which corresponds to a default or general position, and the alternative hypothesis, which asserts a rival relationship. Hypothesis testing uses sample data to evaluate whether differences observed could be due to chance (the null hypothesis) or are real effects (the alternative hypothesis). Key concepts discussed include type 1 and type 2 errors, significance levels, one-sided and two-sided tests, and the relationship between p-values, confidence intervals, and the strength of evidence against

P value, Power, Type 1 and 2 errors

This document discusses key statistical concepts including p-values, type I and II errors, power, and sample size. It defines p-value as the probability of obtaining results as extreme or more extreme than what was observed. Type I error is rejecting the null hypothesis when it is true, while type II error is failing to reject the null when it is false. Power is the probability of avoiding a type II error. The relationships between these concepts and how factors like sample size and effect size influence them are explained. Sample size calculations must consider the desired power, significance level, population variability, and minimum effect size to detect.

Calculating p value

When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. ... The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.

Confidence interval

This document discusses confidence intervals, which provide a range of values that is likely to include an unknown population parameter based on a sample statistic. It defines key concepts like confidence level, confidence limits, and factors that determine how to set the confidence interval like sample size, population variability, and precision of values. It explains how larger sample sizes and more precise measurements result in narrower confidence intervals. Applications to clinical trials are discussed, showing how sample size impacts the ability to make definitive recommendations based on trial results.

Student t-test

The document describes how to perform a student's t-test to compare two samples. It provides steps for both a matched pairs t-test and an independent samples t-test. For a matched pairs t-test, the steps are: 1) state the null and alternative hypotheses, 2) calculate the differences between pairs, 3) calculate the mean difference, 4) calculate the standard deviation of the differences, 5) calculate the standard error, 6) calculate the t value, 7) determine the degrees of freedom, 8) find the critical t value, and 9) determine if there is a statistically significant difference. For an independent samples t-test, similar steps are followed to calculate means, standard deviations, the difference between

P value

This document discusses p-values and their significance in statistical hypothesis testing. It defines a p-value as the probability of obtaining a result equal to or more extreme than what was observed assuming the null hypothesis is true. Lower p-values indicate stronger evidence against the null hypothesis. The document outlines the steps in hypothesis testing which include stating hypotheses, determining acceptable type I and type II error rates, selecting a statistical test to calculate a test statistic, determining the p-value, making inferences, and forming conclusions. It emphasizes that statistical significance does not necessarily imply real-world significance.

Statistical Power

Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.

Sample size determination

A sample design is a definite plan for obtaining a sample from a given population. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.

Hypothesis testing and p values 06

This document discusses hypothesis testing and p-values. It begins by defining a hypothesis as a proposition or prediction about the outcome of an experiment. Hypotheses are formulated and tested through science to evaluate their credibility. There are two main types of hypotheses: the null hypothesis, which corresponds to a default or general position, and the alternative hypothesis, which asserts a rival relationship. Hypothesis testing uses sample data to evaluate whether differences observed could be due to chance (the null hypothesis) or are real effects (the alternative hypothesis). Key concepts discussed include type 1 and type 2 errors, significance levels, one-sided and two-sided tests, and the relationship between p-values, confidence intervals, and the strength of evidence against

P value, Power, Type 1 and 2 errors

This document discusses key statistical concepts including p-values, type I and II errors, power, and sample size. It defines p-value as the probability of obtaining results as extreme or more extreme than what was observed. Type I error is rejecting the null hypothesis when it is true, while type II error is failing to reject the null when it is false. Power is the probability of avoiding a type II error. The relationships between these concepts and how factors like sample size and effect size influence them are explained. Sample size calculations must consider the desired power, significance level, population variability, and minimum effect size to detect.

Calculating p value

When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. ... The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.

Confidence interval

This document discusses confidence intervals, which provide a range of values that is likely to include an unknown population parameter based on a sample statistic. It defines key concepts like confidence level, confidence limits, and factors that determine how to set the confidence interval like sample size, population variability, and precision of values. It explains how larger sample sizes and more precise measurements result in narrower confidence intervals. Applications to clinical trials are discussed, showing how sample size impacts the ability to make definitive recommendations based on trial results.

Student t-test

The document describes how to perform a student's t-test to compare two samples. It provides steps for both a matched pairs t-test and an independent samples t-test. For a matched pairs t-test, the steps are: 1) state the null and alternative hypotheses, 2) calculate the differences between pairs, 3) calculate the mean difference, 4) calculate the standard deviation of the differences, 5) calculate the standard error, 6) calculate the t value, 7) determine the degrees of freedom, 8) find the critical t value, and 9) determine if there is a statistically significant difference. For an independent samples t-test, similar steps are followed to calculate means, standard deviations, the difference between

P value

This document discusses p-values and their significance in statistical hypothesis testing. It defines a p-value as the probability of obtaining a result equal to or more extreme than what was observed assuming the null hypothesis is true. Lower p-values indicate stronger evidence against the null hypothesis. The document outlines the steps in hypothesis testing which include stating hypotheses, determining acceptable type I and type II error rates, selecting a statistical test to calculate a test statistic, determining the p-value, making inferences, and forming conclusions. It emphasizes that statistical significance does not necessarily imply real-world significance.

Statistical Power

Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.

Sample size determination

A sample design is a definite plan for obtaining a sample from a given population. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.

Odds ratio and confidence interval

This document defines odds ratio and describes how to calculate and interpret it. An odds ratio measures the association between two events and compares the odds of one event occurring given the presence or absence of the other event. The document provides an example to calculate the odds ratio to determine if having a mutated gene increases the odds of cancer. It also defines confidence intervals and how they provide a range of values that likely contain the true population parameter based on a sample. Confidence intervals allow flexible data analysis and meaningful conclusions, especially for small sample studies.

types of hypothesis

The presentation discusses null and alternative hypotheses. The null hypothesis expresses no difference or inequality between variables, while the alternative hypothesis expresses a difference or conflict. The null hypothesis is what researchers expect will definitely happen, while the alternative hypothesis is what researchers want to test. Examples are provided of null hypotheses stating children who eat oily fish do not show higher IQ increases than others, and that extroverts and introverts are equally healthy. The alternative hypotheses are that children eating oily fish will show higher IQ increases, and that introverts are not healthier than extroverts.

Randomisation techniques

Randomization is a key process in clinical trials that assigns participants to treatment groups in a way that limits bias. It aims to balance groups so they are similar in all ways except for the intervention received. Common randomization methods include coin tossing, random number tables, and computer generation of sequences. Block and stratified randomization can help produce balanced groups with comparable characteristics. Blinding of participants, investigators, and assessors is important to prevent biases from influencing outcomes. Inclusion and exclusion criteria define who can participate in a clinical trial based on factors like age, sex, disease characteristics, and medical history.

Sample size and power

1. Sample size planning is important as it specifies outcome variables, clinically meaningful effect sizes, statistical procedures, recruitment goals, timelines and budgets.
2. Estimating sample size requires specifying hypotheses, statistical tests, minimum detectable effect sizes, outcome variability, and Type I and II error rates.
3. Software can help estimate sample sizes for different study designs, while smaller samples may be feasible with adjustments like changing error rates, hypotheses, effect sizes, or outcome measures.

PROCEDURE FOR TESTING HYPOTHESIS

This document outlines the process of hypothesis testing. It begins with defining key terms like the null hypothesis (H0), alternative hypothesis (H1), significance level, test statistic, critical value, and decision rule. It then explains the steps involved: 1) setting up H0 and H1, 2) choosing a significance level, 3) calculating the test statistic, 4) finding the critical value, and 5) making a decision by comparing the test statistic and critical value. The overall goal of hypothesis testing is to evaluate claims about a population parameter based on a sample's data.

PPT on Sample Size, Importance of Sample Size,

This document discusses factors related to determining sample size for research studies. It defines key terms like sample size, population and importance of sample size. The selection of sample size involves planning the study, specifying parameters, choosing an effect size, and computing the sample size based on those factors. Sample size is influenced by expected effect size, study power, heterogeneity, error risk, and other variables. Dropouts from the sample during a study also impact sample size calculations. Proper determination of sample size is important for obtaining meaningful results and conducting ethical research.

Power Analysis and Sample Size Determination

This document discusses power analysis and sample size determination. It explains key concepts like power, effect size, significance level, and how changing these factors impacts the required sample size. Sample size is important to correctly power a study to detect clinically meaningful effects without excessive subjects. The document provides formulas and examples for calculating sample sizes for various study designs including randomized trials, pre-post, and equivalence studies. Researchers must consider these factors before collecting data to ensure their study is appropriately powered.

Bias in clinical research

This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...

Bias and errors

This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.

Sample Size Estimation

This document discusses sample size estimation and the factors that influence determining an appropriate sample size for research studies. It provides examples of calculating sample sizes based on prevalence of a disease, mean values, standard deviations, permissible errors, and confidence levels. The key points are:
- Sample size depends on prevalence/magnitude of the attribute being studied, permissible error, and power of the statistical test
- Larger sample sizes are needed to detect smaller differences and have sufficient power
- Examples are provided to demonstrate calculating sample sizes based on prevalence of anemia, mean blood pressure values, and acceptable margins of error

Hypothesis Testing

BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.

Study design in research

This document discusses different study designs used in research. It defines a study design as a specific plan for conducting a study that allows the investigator to translate a conceptual hypothesis into an operational one. The document outlines different types of study designs including descriptive studies, analytical observational studies like cross-sectional studies, case-control studies, and cohort studies, as well as experimental/interventional studies. For each study design, it provides details on the unit of study, study question, direction of inquiry, and key aspects of the design.

Hypothesis Testing

The document discusses the similarities between statistical hypothesis testing and judicial decision making. Both involve making dichotomous decisions (e.g. guilty/not guilty, different/not different) where there are four possible outcomes. The default position for both is "not guilty" or failing to reject the null hypothesis. Both processes aim to minimize Type 1 errors (false positives) by establishing standards of evidence required to reject the default.

Study designs

This document discusses various study designs used in medical research. It describes descriptive study designs like case reports, case series, ecological studies, and cross-sectional studies which are used to describe characteristics of subjects. It also describes analytical study designs like case-control studies and cohort studies which are used to analyze associations between exposures and outcomes. Experimental study designs like randomized controlled trials are also discussed which are used to evaluate interventions. Key aspects of each study design like their strengths, weaknesses and steps are highlighted.

Hypothesis testing ppt final

This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.

How to read a receiver operating characteritic (ROC) curve

1) The document discusses how to evaluate the accuracy of diagnostic tests using receiver operating characteristic (ROC) curves.
2) ROC curves plot the sensitivity of a test on the y-axis against 1-specificity on the x-axis. The area under the ROC curve (AUC) provides an overall measure of a test's accuracy, with higher values indicating better accuracy.
3) The document uses ferritin testing to diagnose iron deficiency anemia (IDA) in the elderly as a case example. The AUC for ferritin was found to be 0.91, indicating it is an excellent test for diagnosing IDA.

Sample determinants and size

The document discusses sample size determination for clinical and epidemiological research. It explains that proper sample size is important for validity, accuracy, and reliability of research findings. Key factors to consider in sample size calculations include the study objective, details of the intervention, outcomes, covariates, research design, and study subjects. Precision analysis and power analysis are two common approaches, with power analysis being most suitable for studies aiming to detect an effect. The document provides formulas and examples for calculating sample sizes for comparative and descriptive studies with both continuous and dichotomous outcomes. It also discusses the concepts of type I and II errors and their relationship to statistical power.

Statistical tests of significance and Student`s T-Test

Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained

Basics of statistics

This document provides an overview of basic statistical concepts and hypothesis testing. It defines biostatistics as the application of statistics to health studies. It describes different types of statistical tests including one-sample and two-sample tests. It explains how to properly design studies and analyze data using descriptive statistics and statistical inference. It also outlines the key components of hypothesis testing including the null and alternative hypotheses, significance levels, test statistics, and how to interpret results.

Test of significance

The document discusses various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It provides information on descriptive statistics, inferential statistics, and the Gaussian distribution. Key tests covered include the z-test, t-test, chi-square test, ANOVA, and their appropriate uses and calculations. Examples are given to illustrate how to apply and interpret each test.

Lesson p values

This document discusses statistical significance and p-values. It explains that statistical significance determines whether differences in experimental and control groups are real or due to chance. Tests of significance are used to measure the influence of chance, and results are considered statistically significant if p < 0.05, meaning there is less than a 5% probability the results are due to chance. The document provides examples of interpreting p-values in experiments.

Hypothesis Testing-Z-Test

Here are the steps to solve this problem:
1. State the hypotheses:
H0: μ = 100
H1: μ ≠ 100
2. The critical values are ±1.96 (two-tailed test, α=0.05)
3. Compute the test statistic:
z = (140 - 100)/15/√40 = 20/15/2 = 4
4. The test statistic is in the critical region, so reject the null hypothesis.
5. There is strong evidence that the medication affected intelligence since the sample mean is much higher than the population mean.

Odds ratio and confidence interval

This document defines odds ratio and describes how to calculate and interpret it. An odds ratio measures the association between two events and compares the odds of one event occurring given the presence or absence of the other event. The document provides an example to calculate the odds ratio to determine if having a mutated gene increases the odds of cancer. It also defines confidence intervals and how they provide a range of values that likely contain the true population parameter based on a sample. Confidence intervals allow flexible data analysis and meaningful conclusions, especially for small sample studies.

types of hypothesis

The presentation discusses null and alternative hypotheses. The null hypothesis expresses no difference or inequality between variables, while the alternative hypothesis expresses a difference or conflict. The null hypothesis is what researchers expect will definitely happen, while the alternative hypothesis is what researchers want to test. Examples are provided of null hypotheses stating children who eat oily fish do not show higher IQ increases than others, and that extroverts and introverts are equally healthy. The alternative hypotheses are that children eating oily fish will show higher IQ increases, and that introverts are not healthier than extroverts.

Randomisation techniques

Randomization is a key process in clinical trials that assigns participants to treatment groups in a way that limits bias. It aims to balance groups so they are similar in all ways except for the intervention received. Common randomization methods include coin tossing, random number tables, and computer generation of sequences. Block and stratified randomization can help produce balanced groups with comparable characteristics. Blinding of participants, investigators, and assessors is important to prevent biases from influencing outcomes. Inclusion and exclusion criteria define who can participate in a clinical trial based on factors like age, sex, disease characteristics, and medical history.

Sample size and power

1. Sample size planning is important as it specifies outcome variables, clinically meaningful effect sizes, statistical procedures, recruitment goals, timelines and budgets.
2. Estimating sample size requires specifying hypotheses, statistical tests, minimum detectable effect sizes, outcome variability, and Type I and II error rates.
3. Software can help estimate sample sizes for different study designs, while smaller samples may be feasible with adjustments like changing error rates, hypotheses, effect sizes, or outcome measures.

PROCEDURE FOR TESTING HYPOTHESIS

This document outlines the process of hypothesis testing. It begins with defining key terms like the null hypothesis (H0), alternative hypothesis (H1), significance level, test statistic, critical value, and decision rule. It then explains the steps involved: 1) setting up H0 and H1, 2) choosing a significance level, 3) calculating the test statistic, 4) finding the critical value, and 5) making a decision by comparing the test statistic and critical value. The overall goal of hypothesis testing is to evaluate claims about a population parameter based on a sample's data.

PPT on Sample Size, Importance of Sample Size,

This document discusses factors related to determining sample size for research studies. It defines key terms like sample size, population and importance of sample size. The selection of sample size involves planning the study, specifying parameters, choosing an effect size, and computing the sample size based on those factors. Sample size is influenced by expected effect size, study power, heterogeneity, error risk, and other variables. Dropouts from the sample during a study also impact sample size calculations. Proper determination of sample size is important for obtaining meaningful results and conducting ethical research.

Power Analysis and Sample Size Determination

This document discusses power analysis and sample size determination. It explains key concepts like power, effect size, significance level, and how changing these factors impacts the required sample size. Sample size is important to correctly power a study to detect clinically meaningful effects without excessive subjects. The document provides formulas and examples for calculating sample sizes for various study designs including randomized trials, pre-post, and equivalence studies. Researchers must consider these factors before collecting data to ensure their study is appropriately powered.

Bias in clinical research

This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...

Bias and errors

This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.

Sample Size Estimation

This document discusses sample size estimation and the factors that influence determining an appropriate sample size for research studies. It provides examples of calculating sample sizes based on prevalence of a disease, mean values, standard deviations, permissible errors, and confidence levels. The key points are:
- Sample size depends on prevalence/magnitude of the attribute being studied, permissible error, and power of the statistical test
- Larger sample sizes are needed to detect smaller differences and have sufficient power
- Examples are provided to demonstrate calculating sample sizes based on prevalence of anemia, mean blood pressure values, and acceptable margins of error

Hypothesis Testing

BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.

Study design in research

This document discusses different study designs used in research. It defines a study design as a specific plan for conducting a study that allows the investigator to translate a conceptual hypothesis into an operational one. The document outlines different types of study designs including descriptive studies, analytical observational studies like cross-sectional studies, case-control studies, and cohort studies, as well as experimental/interventional studies. For each study design, it provides details on the unit of study, study question, direction of inquiry, and key aspects of the design.

Hypothesis Testing

The document discusses the similarities between statistical hypothesis testing and judicial decision making. Both involve making dichotomous decisions (e.g. guilty/not guilty, different/not different) where there are four possible outcomes. The default position for both is "not guilty" or failing to reject the null hypothesis. Both processes aim to minimize Type 1 errors (false positives) by establishing standards of evidence required to reject the default.

Study designs

This document discusses various study designs used in medical research. It describes descriptive study designs like case reports, case series, ecological studies, and cross-sectional studies which are used to describe characteristics of subjects. It also describes analytical study designs like case-control studies and cohort studies which are used to analyze associations between exposures and outcomes. Experimental study designs like randomized controlled trials are also discussed which are used to evaluate interventions. Key aspects of each study design like their strengths, weaknesses and steps are highlighted.

Hypothesis testing ppt final

This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.

How to read a receiver operating characteritic (ROC) curve

1) The document discusses how to evaluate the accuracy of diagnostic tests using receiver operating characteristic (ROC) curves.
2) ROC curves plot the sensitivity of a test on the y-axis against 1-specificity on the x-axis. The area under the ROC curve (AUC) provides an overall measure of a test's accuracy, with higher values indicating better accuracy.
3) The document uses ferritin testing to diagnose iron deficiency anemia (IDA) in the elderly as a case example. The AUC for ferritin was found to be 0.91, indicating it is an excellent test for diagnosing IDA.

Sample determinants and size

The document discusses sample size determination for clinical and epidemiological research. It explains that proper sample size is important for validity, accuracy, and reliability of research findings. Key factors to consider in sample size calculations include the study objective, details of the intervention, outcomes, covariates, research design, and study subjects. Precision analysis and power analysis are two common approaches, with power analysis being most suitable for studies aiming to detect an effect. The document provides formulas and examples for calculating sample sizes for comparative and descriptive studies with both continuous and dichotomous outcomes. It also discusses the concepts of type I and II errors and their relationship to statistical power.

Statistical tests of significance and Student`s T-Test

Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained

Basics of statistics

This document provides an overview of basic statistical concepts and hypothesis testing. It defines biostatistics as the application of statistics to health studies. It describes different types of statistical tests including one-sample and two-sample tests. It explains how to properly design studies and analyze data using descriptive statistics and statistical inference. It also outlines the key components of hypothesis testing including the null and alternative hypotheses, significance levels, test statistics, and how to interpret results.

Test of significance

The document discusses various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It provides information on descriptive statistics, inferential statistics, and the Gaussian distribution. Key tests covered include the z-test, t-test, chi-square test, ANOVA, and their appropriate uses and calculations. Examples are given to illustrate how to apply and interpret each test.

Odds ratio and confidence interval

Odds ratio and confidence interval

types of hypothesis

types of hypothesis

Randomisation techniques

Randomisation techniques

Sample size and power

Sample size and power

PROCEDURE FOR TESTING HYPOTHESIS

PROCEDURE FOR TESTING HYPOTHESIS

PPT on Sample Size, Importance of Sample Size,

PPT on Sample Size, Importance of Sample Size,

Power Analysis and Sample Size Determination

Power Analysis and Sample Size Determination

Bias in clinical research

Bias in clinical research

Bias and errors

Bias and errors

Sample Size Estimation

Sample Size Estimation

Hypothesis Testing

Hypothesis Testing

Study design in research

Study design in research

Hypothesis Testing

Hypothesis Testing

Study designs

Study designs

Hypothesis testing ppt final

Hypothesis testing ppt final

How to read a receiver operating characteritic (ROC) curve

How to read a receiver operating characteritic (ROC) curve

Sample determinants and size

Sample determinants and size

Statistical tests of significance and Student`s T-Test

Statistical tests of significance and Student`s T-Test

Basics of statistics

Basics of statistics

Test of significance

Test of significance

Lesson p values

This document discusses statistical significance and p-values. It explains that statistical significance determines whether differences in experimental and control groups are real or due to chance. Tests of significance are used to measure the influence of chance, and results are considered statistically significant if p < 0.05, meaning there is less than a 5% probability the results are due to chance. The document provides examples of interpreting p-values in experiments.

Hypothesis Testing-Z-Test

Here are the steps to solve this problem:
1. State the hypotheses:
H0: μ = 100
H1: μ ≠ 100
2. The critical values are ±1.96 (two-tailed test, α=0.05)
3. Compute the test statistic:
z = (140 - 100)/15/√40 = 20/15/2 = 4
4. The test statistic is in the critical region, so reject the null hypothesis.
5. There is strong evidence that the medication affected intelligence since the sample mean is much higher than the population mean.

Hypothesis

This document provides an overview of hypotheses for a presentation. It begins with learning outcomes which are to explain the meaning and significance of hypotheses, identify types of hypotheses, and illustrate why hypotheses are needed.
The presentation will cover the scientific method, meaning and types of variables, characteristics of good hypotheses, categories of hypotheses including null and alternative, and how to form and test hypotheses. Hypotheses are defined as educated guesses that relate variables and guide research. They must be testable, falsifiable, and contribute to theory. Hypotheses can be categorized by their formulation as null or alternative, by direction as directional or non-directional, and by their derivation as inductive or deductive.

What's Significant? Hypothesis Testing, Effect Size, Confidence Intervals, & ...

This is a lecture that I gave to a Principles of Epidemiology MPH class. It takes a critical look at the use of p-values to judge the strength of evidence, and offers more holistic, informative approaches to interpreting statistical findings such as measures of effect size and confidence intervals.

Test of significance in Statistics

This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.

P values and replication

P Values and Replication: the problem is not what you think
Lecture at MRC Brain Science & Cognition, Cambridge 16 December 2015
Abstract
It has been claimed that there is a crisis of replication in science. Prominent amongst the many factors that have been fingered as being responsible is the humble and ubiquitous P-value. One journal has even gone so far as to ban all inferential statistics. However, it is one thing to banish measures of uncertainty and another to banish uncertainty from your measures. I shall claim that the apparent discrepancy between P-values and posterior probabilities is as much a discrepancy between two approaches to Bayesian inference as it is between frequentist and Bayesian frameworks and that a further problem has been misunderstandings regarding predictive probabilities. I conclude that banning P-values won’t make all published results repeatable and that it is possible undesirable that it should.

Dependent T Test

The document provides information about conducting a dependent t-test, also known as a paired samples t-test. It is used to compare two dependent or related samples, such as the same group measured at two different time points. The test involves calculating a t-statistic based on the mean difference between pairs and comparing it to a critical value from a t-distribution to determine if the difference is statistically significant. Examples are given of research questions and study designs that could use a dependent t-test to analyze data. The steps of the test procedure are outlined, including stating hypotheses, setting an alpha level, calculating the t-statistic, comparing it to critical values, and making a decision about the null hypothesis.

Hypothesis Testing Lesson 1

The document provides an introduction to hypothesis testing, including its real-life applications, key definitions, and structure. It defines hypothesis testing as the process of testing the validity of a statistical hypothesis based on a random sample from a population. The document outlines the common steps in hypothesis testing: 1) stating the null and alternative hypotheses, 2) choosing a significance level, 3) determining the test statistic and decision criteria, 4) rejecting or failing to reject the null hypothesis, and 5) drawing a conclusion. It also defines important terminology like population mean, null and alternative hypotheses, test statistic, significance level, critical region, and p-value. Real-life examples from pharmaceutical testing and legal cases are provided to illustrate the motivation for hypothesis

Tests of significance

This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, type I and type II errors, one-tailed and two-tailed tests, test statistics, p-values, confidence intervals, and parametric vs non-parametric tests. Specific statistical tests covered include the t-test, z-test, ANOVA, chi-square test, and correlation analyses. The document also addresses how sample size affects test power and significance.

MAD Konsep P value dan Confidence Interval

Dokumen tersebut membahas tentang konsep-konsep statistik yang terkait dengan penelitian klinis seperti validitas, reliabilitas, odds ratio, risiko relatif, nilai-p, interval kepercayaan, serta interpretasi hasil penelitian klinis.

Test of significance

The document provides an overview of statistical hypothesis testing and various statistical tests used to analyze quantitative and qualitative data. It discusses types of data, key terms like null hypothesis and p-value. It then outlines the steps in hypothesis testing and describes different tests of significance including standard error of difference between proportions, chi-square test, student's t-test, paired t-test, and ANOVA. Examples are provided to demonstrate how to apply these statistical tests to determine if differences observed in sample data are statistically significant.

Test of hypothesis

The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,

z-test

This document provides an overview of the Z test for two sample means. It defines the Z test, outlines when it is used, and provides the formula and steps to conduct a hypothesis test using the Z test. An example problem is included that tests if there is a significant difference in average monthly family incomes between two neighborhoods using census data from random samples of 100 families each.

Z tests test statistic

This document discusses z-tests and provides examples of how to perform z-tests to test differences between population and sample means. It explains how to test:
- Population or hypothesized mean vs sample mean
- Two sample means when standard deviations are known
- Two sample means when the population standard deviation is known
It then provides three examples applying z-tests: testing if a sample mean is higher than a population mean, comparing two teaching methods, and comparing exam scores between traditional and technology-based teaching methods. The last example asks the reader to help verify the hypothesis at α=0.05 level of significance.

Hypothesis testing examples on z test

This document provides examples and explanations of common hypothesis testing techniques including:
- Z tests for large samples with known population variance to test claims about population means
- T tests for small samples with unknown population variance
- Tests comparing two population means using Z tests for large samples and T tests for small samples
- One-tailed and two-tailed tests at various significance levels (e.g. 5%, 10%)
Step-by-step solutions and calculations are shown for multiple examples testing claims about means, differences in means, and whether sample data is consistent with hypothesized population parameters.

T Test For Two Independent Samples

An independent t-test is used to compare the means of two independent groups on a continuous dependent variable. It tests if there is a statistically significant difference between the population means of the two groups. The test assumes the groups are independent, the dependent variable is normally distributed for each group, and the groups have equal variances. To perform the test, the researcher states the hypotheses, sets an alpha level, calculates the t-statistic and degrees of freedom, and determines whether to reject or fail to reject the null hypothesis by comparing the t-statistic to the critical value.

Z test

This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.

Introduction to t-tests (statistics)

The document discusses different types of t-tests used to determine if the means of two samples are statistically significantly different from each other. It describes paired sample t-tests used to compare means when the same subjects are measured before and after a treatment. It also describes two-sample t-tests used to compare independent samples that may have equal or unequal variances, and whether the tests are one-tailed or two-tailed. Examples are provided of interpreting t-test output and determining if differences are statistically significant based on the t-statistic and p-values. Non-parametric alternatives like the Mann-Whitney U test are also briefly mentioned.

Research hypothesis

The document discusses hypotheses in research. It defines a hypothesis as a tentative statement about the relationship between two or more variables. Hypotheses help translate research problems into clear predictions and guide investigation. They provide objectivity, direction for data collection, and goals for researchers. Well-stated hypotheses are testable, consistent with existing knowledge, and help establish a link between theory and empirical research. Different types of hypotheses, such as simple, complex, associative, causal, directional, and null hypotheses are described. Sources for developing hypotheses include theoretical frameworks, previous research findings, literature, and experiences.

T test

The t-test is used to compare the means of two groups and has three main applications:
1) Compare a sample mean to a population mean.
2) Compare the means of two independent samples.
3) Compare the values of one sample at two different time points.
There are two main types: the independent-measures t-test for samples not matched, and the matched-pair t-test for samples in pairs. The t-test assumes normal distributions and equal variances between groups. Examples are provided to demonstrate hypothesis testing for each application.

Lesson p values

Lesson p values

Hypothesis Testing-Z-Test

Hypothesis Testing-Z-Test

Hypothesis

Hypothesis

What's Significant? Hypothesis Testing, Effect Size, Confidence Intervals, & ...

What's Significant? Hypothesis Testing, Effect Size, Confidence Intervals, & ...

Test of significance in Statistics

Test of significance in Statistics

P values and replication

P values and replication

Dependent T Test

Dependent T Test

Hypothesis Testing Lesson 1

Hypothesis Testing Lesson 1

Tests of significance

Tests of significance

MAD Konsep P value dan Confidence Interval

MAD Konsep P value dan Confidence Interval

Test of significance

Test of significance

Test of hypothesis

Test of hypothesis

z-test

z-test

Z tests test statistic

Z tests test statistic

Hypothesis testing examples on z test

Hypothesis testing examples on z test

T Test For Two Independent Samples

T Test For Two Independent Samples

Z test

Z test

Introduction to t-tests (statistics)

Introduction to t-tests (statistics)

Research hypothesis

Research hypothesis

T test

T test

Evidence Based Diagnosis

The document discusses key concepts for evaluating diagnostic tests and techniques, including sensitivity, specificity, predictive values, and likelihood ratios. It emphasizes that diagnostic tests need to be evaluated based on their relevance, validity, and ability to help clinicians care for patients. New diagnostic tests should be properly evaluated through clinical studies using gold standard references and accounting for prevalence, blinding, and independent application of the reference standard before being adopted into routine care.

Hypothesis

Hypothesis testing involves developing a null hypothesis (H0) and an alternative hypothesis (Ha) to test a given situation. H0 states there is no difference, while Ha states there is a difference. Tests can be one-tailed or two-tailed. A two-tailed test rejects H0 if the sample mean is significantly different in either direction, while a one-tailed test only rejects if the difference is in the direction specified by Ha. When conducting a test, there is a risk of making a Type I error by rejecting a true H0, or a Type II error by failing to reject a false H0. The significance level determines the probability of a Type I error.

Research by MAGIC

This document provides guidance on key principles for conducting rigorous statistical analysis and research. It discusses the importance of clearly articulating the story being told with the data through use of graphs and tables. Variables of interest, outcomes, and potential confounding factors should be identified. The generalizability and interestingness of results are important to consider. Prospective studies are preferable to retrospective studies, which require consideration of multiple factors to establish credibility. Multiple statistical tests on a single data set require adjustments to avoid inflated false positive rates. Data collection and coding should be done consistently to allow for proper analysis. Overall, the document emphasizes the need for thoughtful statistical methodology to ensure useful and meaningful results.

UAB Pulmonary board review study design and statistical principles

Annotated slides from a lecture on study design and statistical principles for the UAB Pulmonary Medicine Board Review course.

Testing of hypothesis

This document discusses hypothesis testing, including:
1. A hypothesis is a statement that may or may not be true and is tested using sample data. Examples of hypotheses are provided.
2. Hypotheses need to be tested to predict future events and obtain testable results rather than hypothetical results.
3. Key concepts in hypothesis testing are discussed, including null and alternative hypotheses, type I and II errors, one-tailed and two-tailed tests, and descriptive and relational hypotheses.
4. Guidelines for formulating a hypothesis are given. Hypotheses should be simple, test a single relationship, be measurable, and be based on existing literature rather than feelings.

Hypothesis testing and p values 06

This document discusses hypothesis testing and p-values. It defines a hypothesis as a proposition or prediction about the outcome of an experiment. Hypotheses are tested to evaluate their credibility against observed data. There are two main types of hypotheses: the null hypothesis, which corresponds to a default or general position, and the alternative hypothesis, which asserts a relationship different from the null. Errors in hypothesis testing can occur if the decision to reject or fail to reject the null hypothesis is wrong. The p-value indicates how likely the observed or more extreme results would be if the null hypothesis were true. A lower p-value provides stronger evidence against the null hypothesis.

Medical Statistics Pt 1

Fastbleep Academic Masterclass - 31 May 2011
Overview of the use of statistics and statistical error.

Umeapresjr

Quantitative research quantifies outcomes and findings and publishes results including measures of uncertainty and variability. It summarizes data from samples and studies to make generalizations about populations while accounting for sampling uncertainty. Key aspects include defining primary and secondary outcomes, avoiding multiple testing to reduce false positives, and selecting covariates for analysis based on clinical knowledge rather than statistical testing to control for confounding. Publishing results should provide all relevant information to allow others to properly evaluate the work.

Testing Hypothesis

This document provides an overview of basic hypothesis testing concepts. It defines key terms like the null hypothesis, type I and type II errors, significance levels, and p-values. It explains how hypothesis tests are used to determine if there is a statistically significant difference between two groups, with the goal of rejecting or failing to reject the null hypothesis. Examples are given around comparing the effectiveness of two drugs and testing if reindeer can fly. Both parametric and non-parametric statistical tests are introduced.

Hypothesis testing

Statistics is used to interpret data and draw conclusions about populations based on sample data. Hypothesis testing involves evaluating two statements (the null and alternative hypotheses) about a population using sample data. A hypothesis test determines which statement is best supported.
The key steps in hypothesis testing are to formulate the hypotheses, select an appropriate statistical test, choose a significance level, collect and analyze sample data to calculate a test statistic, determine the probability or critical value associated with the test statistic, and make a decision to reject or fail to reject the null hypothesis based on comparing the probability or test statistic to the significance level and critical value.
An example tests whether the proportion of internet users who shop online is greater than 40% using

Testing of Hypothesis.pptx

This document discusses hypothesis testing procedures. It begins by introducing hypothesis testing and defining key terms like the null hypothesis and alternative hypothesis. It then outlines the typical steps in hypothesis testing: 1) formulating the hypotheses, 2) setting the significance level, 3) choosing a test criterion, 4) performing computations, and 5) making a decision. It also discusses concepts like type I and type II errors, and one-tailed vs two-tailed tests. Tail tests refer to whether the rejection region is in one tail or both tails of the sampling distribution. The document provides examples and explanations of these statistical hypothesis testing concepts.

Chapter 4(1) Basic Logic

1. The document discusses hypothesis testing for continuous variables using a t-test. It provides an example of using a t-test to determine if the mean blood sedimentation of patients differs from a reported value in literature.
2. The main steps of hypothesis testing are outlined: setting up null and alternative hypotheses, selecting a test statistic, determining the p-value, making a decision to reject or not reject the null hypothesis based on the p-value.
3. An example t-test is provided to determine if the mean pulse of healthy males in a mountainous area differs from the reported national average, finding the means are statistically significantly different.

Research methodology iii

The document discusses key concepts related to research methodology and hypothesis testing. It defines the following:
- Null and alternative hypotheses, with the null hypothesis representing what is being tested and the alternative representing other possibilities.
- Type I and Type II errors in hypothesis testing, with Type I being rejection of a true null hypothesis and Type II being acceptance of a false null hypothesis.
- Significance levels which determine the probability of a Type I error, with common values being 0.10, 0.05, and 0.01.
- Power which is the probability of correctly rejecting a false null hypothesis and can be increased by raising the significance level, increasing sample size, or considering alternatives further from the null.

Chapter 4(1) Basic Logic

The document discusses hypothesis testing for continuous variables. It covers the specific logic and main steps of hypothesis testing, including setting up the null and alternative hypotheses, selecting a test statistic, determining the p-value, making a decision to reject or not reject the null hypothesis, and drawing a conclusion. An example is provided to illustrate the process of conducting a t-test for one group of data to test if a sample mean is significantly different from a hypothesized population mean.

Chapter 4(1) Basic Logic

The document discusses hypothesis testing for continuous variables. It provides examples to illustrate the specific logic and main steps of hypothesis testing, which include setting up the null and alternative hypotheses, selecting a test statistic, calculating its value, determining the p-value, making a decision to reject or not reject the null hypothesis based on the p-value, and stating a conclusion. The t-test is introduced for testing hypotheses about population means using sample means and standard deviations. Examples are provided to demonstrate applying the t-test to test if a sample mean is significantly different from a hypothesized population mean.

P-values the gold measure of statistical validity are not as reliable as many...

This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.

introduction to biostatistics in clinical trials

This document provides an introduction to biostatistics for clinical research. It discusses key concepts such as:
- Estimation and hypothesis testing using statistical inference to make inferences about populations based on samples
- Common analyses used in clinical trials including comparing means, proportions, odds ratios, and survival analysis
- Important considerations for sample size calculations such as type I and type II errors, variance, and detecting a clinically meaningful difference.

introduction to biostatistics in clinical trials

This document provides an introduction to biostatistics for clinical research. It discusses key concepts such as:
- Estimation and hypothesis testing using statistical inference to make conclusions about populations based on sample data.
- Types of errors in hypothesis testing, including Type I errors of rejecting the null hypothesis when it is true and Type II errors of failing to reject it when it is false.
- Common statistical analyses used in clinical trials such as comparing means, calculating odds ratios, and survival analyses to compare time to an event between groups.
- Factors considered for sample size calculations like power, Type I and Type II errors, variance, and clinically relevant differences.

Abc4

The document discusses key concepts in statistics and research methodology. It addresses (1) different types of uncertainty including random variation from measurement errors and sampling variation, as well as systematic biases. It also discusses (2) the importance of defining the target population when making inferences from sample data and limitations when only one experiment is conducted. Finally, it covers (3) key issues like confounding bias, differences between experimental and observational studies, and the importance of validity over significance testing.

HYPOTHESIS TESTS.pptx

This document discusses various statistical hypothesis tests including z-tests, t-tests, and F-tests. It provides information on null and alternative hypotheses, type I and type II errors, and one-tailed and two-tailed tests. Specific z-test and t-test procedures are described for testing means, differences between means, related samples, and proportions. Examples of hypothesis, test statistics, and test conditions are given for each.

Evidence Based Diagnosis

Evidence Based Diagnosis

Hypothesis

Hypothesis

Research by MAGIC

Research by MAGIC

UAB Pulmonary board review study design and statistical principles

UAB Pulmonary board review study design and statistical principles

Testing of hypothesis

Testing of hypothesis

Hypothesis testing and p values 06

Hypothesis testing and p values 06

Medical Statistics Pt 1

Medical Statistics Pt 1

Umeapresjr

Umeapresjr

Testing Hypothesis

Testing Hypothesis

Hypothesis testing

Hypothesis testing

Testing of Hypothesis.pptx

Testing of Hypothesis.pptx

Chapter 4(1) Basic Logic

Chapter 4(1) Basic Logic

Research methodology iii

Research methodology iii

Chapter 4(1) Basic Logic

Chapter 4(1) Basic Logic

Chapter 4(1) Basic Logic

Chapter 4(1) Basic Logic

P-values the gold measure of statistical validity are not as reliable as many...

P-values the gold measure of statistical validity are not as reliable as many...

introduction to biostatistics in clinical trials

introduction to biostatistics in clinical trials

introduction to biostatistics in clinical trials

introduction to biostatistics in clinical trials

Abc4

Abc4

HYPOTHESIS TESTS.pptx

HYPOTHESIS TESTS.pptx

Spectacle frame selection

The document discusses selecting spectacle frames. It recommends finding the right position by ensuring the eyes look through the center of each lens and the frame feels comfortable. It also stresses matching the frame size and shape to one's face, noting frames come in petite, narrow, medium and wide sizes and different shapes suit oval, round, square and heart-shaped faces. Color is another factor to consider in frame selection.

Special purpose frames

This document discusses different types of special purpose frames. It describes frames that hold supplementary lenses outside the main frame, frames that contain cells to hold additional lenses behind the prescription, and folding frames with hinges at the bridge and temples to reduce the frame size. It also covers frames with extensions to support the lower eyelid, trial frames without temples, monocular frames that allow viewing through one lens at a time, and frames with flip-down lenses for reading or sunglasses.

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 1

This document discusses key concepts for evaluating diagnostic tests, including accuracy, precision, sensitivity, specificity, and predictive values. It defines a diagnostic test as one that provides evidence for or against a pathology. Key factors for evaluating tests are described as accuracy, precision, sensitivity, specificity, and predictive values. Accuracy refers to how close a test value is to the gold standard, while precision refers to reproducibility of values. Sensitivity and specificity measure a test's ability to correctly identify diseases and non-diseases. Predictive values measure the probability that a positive or negative test result correctly identifies a disease or non-disease. Examples are provided to illustrate these concepts.

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 2

This document discusses key concepts for evaluating diagnostic tests, including sensitivity, specificity, and predictive values. It explains how the criterion used to classify test results can affect sensitivity and specificity, as shown by receiver operating characteristic (ROC) curves. ROC curves summarize how criteria impact the trade-off between sensitivity and specificity. Likelihood ratios quantify this relationship and are used to calculate pre-test and post-test probabilities of disease. The sequential use of multiple diagnostic tests with different likelihood ratios can progressively update the probability that a patient has a disease.

Frames and frame measurements__optometry_india_eyenirvaan.com

This document discusses different parts of eyeglass frames including nose bridges and nose pads. It describes saddle, modified saddle, and keyhole nose bridge designs. Nose pads are classified by shape, size, and material. Common materials include silicone, PVC, and polycarbonate. Frame measurements like horizontal width, lens separation, temple length, and splay angle are also defined. The document aims to provide information on classifying and measuring different parts of eyeglass frames.

Bifocals_optometry india_eyenirvaan

The document provides contact information for an optometrist, Fakhruddin Barodawala, and mentions bifocals. It directs the reader to a website, www.eyenirvaan.com, multiple times to view more presentations and articles on bifocals and related topics. Diagrams of bifocal lens components such as the main lens, support segment, and cover lens are displayed.

Leprosy part 2 - a presentation at www.eyenirvaan.com

This document discusses ocular complications of leprosy, including potential blinding lesions. It describes how various structures of the eye can be involved like the cornea, iris, and lens. Complications discussed include lagophthalmos, exposure keratitis, corneal hypoesthesia, acute iritis, scleritis, and cataracts. Treatment options are provided for each complication. The document emphasizes the importance of early diagnosis and treatment of leprosy and reactions to prevent blindness.

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Leprosy, also known as Hansen's disease, is a chronic infection caused by the bacterium Mycobacterium leprae. It primarily affects the skin and peripheral nerves. There are three main classifications of leprosy - tuberculoid, lepromatous, and borderline. Tuberculoid leprosy presents as asymmetric skin lesions and peripheral nerve involvement. Lepromatous leprosy presents as diffuse macular and nodular skin lesions. Borderline leprosy has features between the other two classifications. Treatment involves a three drug regimen of rifampicin, clofazimine, and dapsone. Prevention strategies include avoiding contact, ensuring regular treatment and follow up

Introduction to ocular anatomy and physiology -a presentation at www.eyenirva...

This presentation provides an overview of ocular anatomy and physiology. It begins by defining anatomy and physiology, with anatomy describing bodily structures and physiology describing how the body functions. Key points about the eye are provided, including that it acts like a camera and converts light signals to neural signals. Basic anatomical concepts and the four basic tissues - epithelium, connective, muscle and nervous tissue - are reviewed. Important physiological concepts such as cellular structures, transport mechanisms, and intercellular junctions are also summarized. The external anatomy of the eye, internal chambers, accessory structures and detailed anterior anatomy are depicted. Accommodation of the lens and anatomy and physiology of rods and cones are described. The visual pathway from the eye to the brain is

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Leprosy, also known as Hansen's disease, is a chronic infection caused by the bacterium Mycobacterium leprae. It primarily affects the skin and peripheral nerves. There are three main classifications of leprosy - tuberculoid, lepromatous, and borderline. Tuberculoid leprosy presents as asymmetric skin lesions and peripheral nerve involvement. Lepromatous leprosy presents as diffuse macular and nodular skin lesions. Borderline leprosy has features between the other two classifications. Treatment involves a three drug regimen of rifampicin, clofazimine, and dapsone. Prevention strategies include avoiding contact, ensuring regular treatment and follow up

Leprosy - Part 2 - a presentation at www.eyenirvaan.com

This document discusses ocular complications of leprosy, including potential blinding lesions. It describes how various structures of the eye can be involved like the cornea, iris, and lens. Complications discussed in detail include lagophthalmos, exposure keratitis, corneal hypoesthesia, acute iritis, scleritis, and cataracts. Treatment options are provided for each complication. The document emphasizes the importance of early diagnosis and treatment of leprosy and reactions to prevent blindness.

Diabetes melitis & eye part 2 presentation at www.eyenirvaan.com

This document discusses diabetic retinopathy and its stages: background diabetic retinopathy, pre-proliferative diabetic retinopathy, proliferative diabetic retinopathy, and advanced diabetic eye disease. It covers the signs, symptoms, management, and treatment for each stage. The document also discusses other common diabetic eye complications like retinal occlusive diseases, optic disc issues, glaucoma, and cranial nerve palsies. The role of optometrists in screening, referring, and low vision care for patients with diabetic eye disease is highlighted.

Diabetes melitis & eye part 1 presentation at www.eyenirvaan.com

This document provides an overview of diabetes mellitus and its effects on the eye. It discusses how diabetes impacts various parts of the eye including the optics, lids, conjunctiva, cornea, iris, lens, accommodation, intraocular pressure, vitreous, retina, optic disc and glaucoma. Specific retinal conditions caused by diabetes like diabetic retinopathy and maculopathy are explained in detail. Treatment options for diabetic macular edema like laser photocoagulation and its focal versus grid approaches are also summarized.

Cl history taking a presentation for eyenirvaan.com

An optometrist must take a thorough patient history before fitting contact lenses to determine if they are a suitable candidate and to rule out any contraindications. The history gathering covers general information, ocular history, previous contact lens wear, medical history, lifestyle and current motivation for contact lenses. For children, the optometrist must consider their maturity and ability to properly handle and care for contact lenses independently. A thorough history is essential for diagnosis, treatment decisions and choosing the appropriate contact lens type and care regimen.

Part 1 fundus imaging – presentation for www.eyenirvaan.com

This document discusses the need for fundus imaging in Indian optometry clinics. It notes that over 75% of blindness in India is avoidable through direct management or early detection of conditions like glaucoma, diabetic retinopathy, and refractive errors. Fundus imaging allows optometrists to monitor these conditions over time by comparing new images to past images. It also provides a wider view of the fundus than direct ophthalmoscopy alone. Non-mydriatic cameras can image up to 40 degrees of the fundus within seconds without pupil dilation. The document emphasizes the growing threat of diabetic retinopathy, as India is projected to see a 72% increase in its diabetic population by 2030. Early detection of diabetic

Part 2 fundus imaging – presentation for www.eyenirvaan.com

This document discusses the benefits of using non-mydriatic fundus imaging in optometry clinics in India. It notes that fundus imaging allows detection of retinal pathologies like diabetic retinopathy without pupil dilation. Images can be easily acquired within 10 minutes and zoomed in on for closer examination. This early detection of conditions like diabetic retinopathy can help prevent vision loss. The document argues that fundus imaging is feasible for Indian optometry clinics as it requires no structural changes and fits within existing clinic space and workflows.

Photochromatic lenses and tints www.eyenirvaan.com

This document discusses photochromatic lenses and lens tints. It describes how photochromics work using either glass or plastic and different manufacturing methods like imbibition and in-mass. Glass photochromics darken fully but don't lighten indoors while plastic may fade over time. Factors like temperature, UV exposure and lens thickness affect performance. Tints are made through coatings using metallic oxides or dyeing plastics, with different colors absorbing distinct light wavelengths to provide benefits like reducing glare or improving contrast.

Prescribing for refractive errors - presentation at www.eyenirvaan.com

This document discusses prescribing for refractive errors such as myopia, pseudomyopia, hypermetropia, astigmatism, and presbyopia. For myopia, optimal correction is recommended with slight undercorrection in some cases. Pseudomyopia is differentiated from true myopia and treated with cycloplegic drops and exercises. Hypermetropia in children under 7 may not require correction if asymptomatic, while adults generally need full correction. Astigmatism is usually fully corrected but some undercorrection is acceptable. Presbyopia is assessed using near tests and treated with reading additions based on age.

Manufacturing methods of soft contact lens - presentation at www.eyenirvaan.com

There are four main methods for manufacturing soft contact lenses: spin casting, lathe cutting, cast molding, and lightstream process. Spin casting involves injecting liquid polymer into a spinning mold to form the lens shape and curing it with UV light. Lathe cutting machines individually grind and polish acrylic buttons into lenses. Cast molding injects liquid monomer into molds that are then cured to form a finished lens. The lightstream process uses molds and masks to form the lens edges during curing. Each method has advantages and disadvantages related to precision, reproducibility, and cost.

Retinoscopy presentation at www.eyenirvaan.com

The document discusses the history and technique of retinoscopy. It describes retinoscopy as an objective method to measure the optical power of the eye. Key developments include the first use of retinoscopy in 1873, the introduction of the streak retinoscope in the early 1900s, and Jack Copeland's accidental invention of the modern retinoscope in 1920. The document provides guidance on proper retinoscopy technique, including characteristics of the fundus reflex, working distance, handling of the instrument, and sources of error.

Spectacle frame selection

Spectacle frame selection

Special purpose frames

Special purpose frames

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 1

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 1

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 2

Evaluating a diagnostic test presentation www.eyenirvaan.com - part 2

Frames and frame measurements__optometry_india_eyenirvaan.com

Frames and frame measurements__optometry_india_eyenirvaan.com

Bifocals_optometry india_eyenirvaan

Bifocals_optometry india_eyenirvaan

Leprosy part 2 - a presentation at www.eyenirvaan.com

Leprosy part 2 - a presentation at www.eyenirvaan.com

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Introduction to ocular anatomy and physiology -a presentation at www.eyenirva...

Introduction to ocular anatomy and physiology -a presentation at www.eyenirva...

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Leprosy - Part 1 - a presentation at www.eyenirvaan.com

Leprosy - Part 2 - a presentation at www.eyenirvaan.com

Leprosy - Part 2 - a presentation at www.eyenirvaan.com

Diabetes melitis & eye part 2 presentation at www.eyenirvaan.com

Diabetes melitis & eye part 2 presentation at www.eyenirvaan.com

Diabetes melitis & eye part 1 presentation at www.eyenirvaan.com

Diabetes melitis & eye part 1 presentation at www.eyenirvaan.com

Cl history taking a presentation for eyenirvaan.com

Cl history taking a presentation for eyenirvaan.com

Part 1 fundus imaging – presentation for www.eyenirvaan.com

Part 1 fundus imaging – presentation for www.eyenirvaan.com

Part 2 fundus imaging – presentation for www.eyenirvaan.com

Part 2 fundus imaging – presentation for www.eyenirvaan.com

Photochromatic lenses and tints www.eyenirvaan.com

Photochromatic lenses and tints www.eyenirvaan.com

Prescribing for refractive errors - presentation at www.eyenirvaan.com

Prescribing for refractive errors - presentation at www.eyenirvaan.com

Manufacturing methods of soft contact lens - presentation at www.eyenirvaan.com

Manufacturing methods of soft contact lens - presentation at www.eyenirvaan.com

Retinoscopy presentation at www.eyenirvaan.com

Retinoscopy presentation at www.eyenirvaan.com

Deerfoot Church of Christ Bulletin 6 16 24

Deerfoot Church of Christ Bulletin 6 16 24

Monthly Khazina-e-Ruhaniyaat Jun’2024 (Vol.15, Issue 2)

2nd issue of Volume 15. A magazine in urdu language mainly based on spiritual treatment and learning. Many topics on ISLAM, SUFISM, SOCIAL PROBLEMS, SELF HELP, PSYCHOLOGY, HEALTH, SPIRITUAL TREATMENT, Ruqya etc.A very useful magazine for everyone.

Astronism, Cosmism and Cosmodeism: the space religions espousing the doctrine...

This lecture created by Brandon Taylorian (aka Cometan) specially for the CESNUR Conference held Bordeaux in June 2024 provides a brief introduction to the legacy of religious and philosophical thought that Astronism emerges from, namely the discourse on transcension started assuredly by the Cosmists in Russia in the mid-to-late nineteenth century and then carried on and developed by Mordecai Nessyahu in Cosmodeism in the twentieth century. Cometan also then provides some detail on his story in founding Astronism in the early twenty-first century from 2013 along with details on the central Astronist doctrine of transcension. Finally, the lecture concludes with some contributions made by space religions and space philosophy and their influences on various cultural facets in art, literature and film.

English - The Book of 1st Samuel the Prophet.pdf

The Book of Samuel is a book in the Hebrew Bible, found as two books in the Old Testament. The book is part of the Deuteronomistic history, a series of books that constitute a theological history of the Israelites and that aim to explain God's law for Israel under the guidance of the prophets.

Trusting God's Providence | Verse: Romans 8: 28-31

Trusting God's Providence.
Providence - God’s active preservation and care over His creation. God is both the Creator and the Sustainer of all things Heb. 1:2-3; Col. 1:17
-God keep His promises.
-God’s general providence is toward all creation
- All things were made through Him
God’s special providence is toward His children.
We may suffer now, but joy can and will come
God can see what we cannot see

Sanatan Vastu | Experience Great Living | Vastu Expert

Santan Vastu Provides Vedic astrology courses & Vastu remedies, If you are searching Vastu for home, Vastu for kitchen, Vastu for house, Vastu for Office & Factory. Best Vastu in Bahadurgarh. Best Vastu in Delhi NCR

Lesson 12 - The Blessed Hope: The Mark of the Christian.pptx

Lesson 12 - The Blessed Hope: The Mark of the Christian
SBS – Sunday Bible School
Adult Bible Lessons 2nd quarter 2024 CPAD
MAGAZINE: THE CAREER THAT IS PROPOSED TO US: The Path of Salvation, Holiness and Perseverance to Reach Heaven
Commentator: Pastor Osiel Gomes
Presentation: Missionary Celso Napoleon
Renewed in Grace

Chandra Dev: Unveiling the Mystery of the Moon God

Shining brightly in the sky, some days more than others, the Moon in popular culture is a symbol of love, romance, and beauty. The ancient Hindu texts, however, mention the Moon as an intriguing and powerful being, worshiped by sages as Chandra.

Is Lucid Dreaming Dangerous? Risks and Benefits!

Lucid Dreaming: Understanding the Risks and Benefits
The ability to control one's dreams or for the dreamer to be aware that he or she is dreaming. This process, called lucid dreaming, has some potential risks as well as many fascinating benefits. However, many people are hesitant to try it initially for fear of the potential dangers. This article aims to clarify these concerns by exploring both the risks and benefits of lucid dreaming.
The Benefits of Lucid Dreaming
Lucid dreaming allows a person to take control of their dream world, helping them overcome their fears and eliminate nightmares. This technique is particularly useful for mental health. By taking control of their dreams, individuals can face challenging scenarios in a controlled environment, which can help reduce anxiety and increase self-confidence.
Addressing Common Concerns
Physical Harm in Dreams Lucid dreaming is fundamentally safe. In a lucid dream, everything is a creation of your mind. Therefore, nothing in the dream can physically harm you. Despite the vividness and realness of the dream experience, it remains entirely within your mental landscape, posing no physical danger.
Mental Health Risks Concerns about developing PTSD or other mental illnesses from lucid dreaming are unfounded. As soon as you wake up, it's clear that the events experienced in the dream were not real. On the contrary, lucid dreaming is often seen as a therapeutic tool for conditions like PTSD, as it allows individuals to reframe and manage their thoughts.
Potential Risks of Lucid Dreaming
While generally safe, lucid dreaming does come with a few risks as well:
Mixing Dream Memories with Reality Long-term lucid dreamers might occasionally confuse dream memories with real ones, creating false memories. This issue is rare and preventable by maintaining a dream journal and avoiding lucid dreaming about real-life people or places too frequently.
Escapism Using lucid dreaming to escape reality can be problematic if it interferes with your daily life. While it is sometimes beneficial to escape and relieve the stress of reality, relying on lucid dreaming for happiness can hinder personal growth and productivity.
Feeling Tired After Lucid Dreaming Some people report feeling tired after lucid dreaming. This tiredness is not due to the dreams themselves but often results from not getting enough sleep or using techniques that disrupt sleep patterns. Taking breaks and ensuring adequate sleep can prevent this.
Mental Exhaustion Lucid dreaming can be mentally taxing if practiced excessively without breaks. It’s important to balance lucid dreaming with regular sleep to avoid mental fatigue.
Lucid dreaming is safe and beneficial if done with caution. It has many benefits, such as overcoming fear and improving mental health, and minimal risks. There are many resources and tutorials available for those interested in trying it.

The Vulnerabilities of Individuals Born Under Swati Nakshatra.pdf

Individuals born under Swati Nakshatra often exhibit a strong sense of independence and adaptability, yet they may also face vulnerabilities such as indecisiveness and a tendency to be easily swayed by external influences. Their quest for balance and harmony can sometimes lead to inner conflict and a lack of assertiveness. To know more visit: astroanuradha.com

Heartfulness Magazine - June 2024 (Volume 9, Issue 6)

Dear readers,
This month we continue with more inspiring talks from the Global Spirituality Mahotsav that was held from March 14 to 17, 2024, at Kanha Shanti Vanam.
We hear from Daaji on lifestyle and yoga in honor of International Day of Yoga, June 21, 2024. We also hear from Professor Bhavani Rao, Dean at Amrita Vishwa Vidyapeetham University, on spirituality in action, the Venerable BhikkuSanghasena on how to be an ambassador for compassion, Dr. Tony Nader on the Maharishi Effect, Swami Mukundananda on the crossroads of modernization, Tejinder Kaur Basra on the purpose of work, the Venerable GesheDorjiDamdul on the psychology of peace, the Rt. Hon. Patricia Scotland, KC, Secretary-General of the Commonwealth, on how we are all related, and world-renowned violinist KumareshRajagopalan on the uplifting mysteries of music.
Dr. Prasad Veluthanar shares an Ayurvedic perspective on treating autism, Dr. IchakAdizes helps us navigate disagreements at work, Sravan Banda celebrates World Environment Day by sharing some tips on land restoration, and Sara Bubber tells our children another inspiring story and challenges them with some fun facts and riddles.
Happy reading,
The editors

Deerfoot Church of Christ Bulletin 6 16 24

Deerfoot Church of Christ Bulletin 6 16 24

Monthly Khazina-e-Ruhaniyaat Jun’2024 (Vol.15, Issue 2)

Monthly Khazina-e-Ruhaniyaat Jun’2024 (Vol.15, Issue 2)

Astronism, Cosmism and Cosmodeism: the space religions espousing the doctrine...

Astronism, Cosmism and Cosmodeism: the space religions espousing the doctrine...

English - The Book of 1st Samuel the Prophet.pdf

English - The Book of 1st Samuel the Prophet.pdf

Trusting God's Providence | Verse: Romans 8: 28-31

Trusting God's Providence | Verse: Romans 8: 28-31

Seminar on Music on the Liturgy Parish .pptx

Seminar on Music on the Liturgy Parish .pptx

Sanatan Vastu | Experience Great Living | Vastu Expert

Sanatan Vastu | Experience Great Living | Vastu Expert

Lesson 12 - The Blessed Hope: The Mark of the Christian.pptx

Lesson 12 - The Blessed Hope: The Mark of the Christian.pptx

Chandra Dev: Unveiling the Mystery of the Moon God

Chandra Dev: Unveiling the Mystery of the Moon God

Is Lucid Dreaming Dangerous? Risks and Benefits!

Is Lucid Dreaming Dangerous? Risks and Benefits!

The Vulnerabilities of Individuals Born Under Swati Nakshatra.pdf

The Vulnerabilities of Individuals Born Under Swati Nakshatra.pdf

Heartfulness Magazine - June 2024 (Volume 9, Issue 6)

Heartfulness Magazine - June 2024 (Volume 9, Issue 6)

- 1. Shrikant R. Bharadwaj BSopt., PhD HYPOTHESIS TESTING AND P – VALUE
- 2. Hypothesis Testing & p-value
- 3. This presentation will cover … • Hypothesis testing • Attributes of a sampling distribution • p-value • Type-I and Type-II errors in hypothesis testing
- 4. What is a Hypothesis? • Hypothesis is a proposed explanation of a phenomenon that can be scientifically tested • Hypothesis is a tentative statement about the relationship between two or more variables that is specific and testable • Evolution Vs. Creation controversy • Organisms evolve from one form to another is a testable hypothesis proposed by Sir Charles Darwin • Organisms were created by a supernatural force (aka God) is a belief and not a testable hypothesis • Tammy Kitzmiller, et al Vs. Dover Area Public School, et al (2005) To view more presentations and articles, visit www.eyenirvaan.com
- 5. What is a Hypothesis? • Hypothesis is a proposed explanation of a phenomenon that can be scientifically tested • Hypothesis is a tentative statement about the relationship between two or more variables that is specific and testable • Drugs that lower IOP reduce retinal ganglion cell loss • Using 3 doses of Avastin injection reduces retinal angiogenesis by 50% • The number of people entering Patodia hall for morning class is maximum between 6:59:50AM and 7:00:00AM To view more presentations and articles, visit www.eyenirvaan.com
- 6. Null Vs. Alternate Hypothesis • Science is all about testing a given hypothesis • Two contradictory hypotheses under consideration - Null Hypothesis (H0) - Alternate Hypothesis (Ha) • Null hypothesis is typically the claim that is initially assumed to the true - It is the default choice • Alternate hypothesis is typically opposite of the Null hypothesis To view more presentations and articles, visit www.eyenirvaan.com
- 7. Examples of Null & Alternate Hypothesis • One is considered innocent unless proven guilty - Null hypothesis (H0): A person accused of murder is innocent - Alternate hypothesis (Ha): This person is guilty of murder • What is the impact of an IOP lowering drug on retinal ganglion cell loss? - H0: Drug lowering IOP has no impact on retinal ganglion cell loss - Ha: Drug lowering IOP has some impact on retinal ganglion cell loss • What is the impact of Avastin on retinal angiogenesis? - H0: Avastin has no impact on angiogensis - Ha: Avastin has some impact on angiogensis • The alternate hypothesis is typically bi-directional (aka two-tailed) To view more presentations and articles, visit www.eyenirvaan.com
- 8. Null Vs. Alternate Hypothesis What is the impact of beta blockers on IOP? Null hypothesis (H0) The IOP in a placebo and beta-blocker treated cohort are not different from each other Alternate hypothesis (Ha) The IOP in the beta-blocker treated cohort is different from the IOP in the placebo cohort Mean of treatment group is lower than the placebo group Lower-tail of the Placebo cohort’s Gaussian distribution
- 9. Null Vs. Alternate Hypothesis • The purpose of a study is to provide evidence for or against the null hypothesis • Based on the evidence gathered by the study, you either support or reject the null hypothesis • Only as a corollary, you reject or support the alternate hypothesis Terminology clarification • You cannot PROVE the null hypothesis; you can only DISPROVE it • Science and hypothesis testing are based on the logic of falsification • http://www.statisticalmisconceptions.com/sample2.html To view more presentations and articles, visit www.eyenirvaan.com
- 10. Proving Vs. Disproving • Null hypothesis: All crows in this world are black • To PROVE the null hypothesis, you need to get the color of every single crow in this world • To DISPROVE the null hypothesis, you just need to show one white crow
- 11. Proving Vs. Providing Evidence • PROVE is a dangerous word – it leaves no room for error!! • (a + b)2 = a2 + b2 + 2ab --------- this can be PROVED mathematically • What is the impact of beta blockers on IOP? • You are NOT PROVING that beta blockers reduce IOP • You are only PROVIDING EVIDENCE that beta blockers can reduce IOP To view more presentations and articles, visit www.eyenirvaan.com
- 12. Proving Vs. Providing Evidence Reasons why biological research cannot PROVE anything 1.Humans react differently to a given treatment 2.Measurement error 3.Data is not obtained from every human being on Earth Biological research can only determine how likely or unlikely a given result is To view more presentations and articles, visit www.eyenirvaan.com
- 13. Sampling a population • Data cannot be obtained from every human being on Earth • A representative cohort is sampled and results from this cohort are extrapolated to the entire population Fully deterministic distribution with no standard deviation Realistic biological distribution with standard deviation
- 14. Properties of a Sampling Distribution μ = Mean of Gaussian distribution; σ = Standard deviation of Gaussian distribution Data from 68.2% of the population falls within +/-1σ Data from 95.4% of the population falls within +/-2σ Data from 99.6% of the population falls within +/-3σ
- 15. Standard Deviation & Confidence Intervals Standard deviation describes variability of measurements in your sample Confidence intervals describe the interval over which the mean will fall when the experiment is repeated multiple times 95% Confidence interval = +/-1.96 SD 99% Confidence interval = +/-2.58 SD To view more presentations and articles, visit www.eyenirvaan.com
- 16. Z-scores Z-score is a unitless quantity that describes how many standard deviations away from the mean is your sample value Z = (x – μ) / σ 1Z-score = 1SD; 2Z-scores = 2SD; 3Z-scores = 3SD
- 17. p-value Biological research aims at determining the likelihood of the null hypothesis being rejected What is the likelihood that a lowered IOP was really due to the treatment and not by chance? p-value (or “probability” value) gives us this likelihood p-value ranges from 0 to 1 or 0% to 100% There can be 0% probability to 100% probability of rejecting the null hypothesis To view more presentations and articles, visit www.eyenirvaan.com
- 18. p-value p-value estimates the false positive rate (Type 1 error) that we are willing to accept Typically, we accept a false-positive rate of <=5% (p <= 0.05) 95% confidence that the IOP value came from the treated distribution 95% confidence that null hypothesis can be rejected 5% (or 1 in 20 times) our results can be incorrect
- 19. p-value p = 0.01 •99% confidence that null hypothesis can be rejected •1% (or 1 in 100 times) our results can be incorrect p = 0.1 •90% confidence that null hypothesis can be rejected •10% (or 1 in 10 times) our results can be incorrect
- 20. Determinants of the p-value p-value is lower when… 1.Mean difference is large 2.Small variance in each distribution Example 1: Non diseased Mean + 1SD: 20 + 5mmHg Diseased Mean + 1SD: 25 + 5mmHg Example 2: Non diseased Mean + 1SD: 20 + 5mmHg Diseased Mean + 1SD: 35 + 5mmHg Example 3: Non diseased Mean + 1SD: 20 + 2mmHg Diseased Mean + 1SD: 35 + 2mmHg p-value of Eg 3 < Eg 2 < Eg 1
- 21. Determinants of the p-value p-value is lower when… 1.Mean difference is large 2.Small variance in each distribution Example 1: Non diseased Mean + 1SD: 20 + 5mmHg Diseased Mean + 1SD: 25 + 5mmHg Example 2: Non diseased Mean + 1SD: 20 + 5mmHg Diseased Mean + 1SD: 35 + 5mmHg Example 3: Non diseased Mean + 1SD: 20 + 2mmHg Diseased Mean + 1SD: 35 + 2mmHg p-value of Eg 3 < Eg 2 < Eg 1
- 22. Use of p-value in a Student’s t-test To view more presentations and articles, visit www.eyenirvaan.com
- 23. Use of p-value in a Student’s t-test • Two kinds of t-tests • Paired t-test: The two datasets are obtained from the same cohort (e.g. IOP before and after treatment with beta-blockers) • Unpaired t-test: The two datasets are obtained on different cohorts (e.g. Body weight of 30 – 40yr old males and females) Practical demo of T-test in MS Excel Large mean difference Small mean difference Large variance p = 0.0325 p = 0.2906 Small variance p < 0.0001 p = 0.1011
- 24. Type-I and Type-II Errors • Type-I error (α error): When the Null hypothesis is true, but it is rejected by the test • Type-I error is equivalent to generating False Positives • Type-II error (β error): When the Null hypothesis is false, but it is erroneously accepted as true • Type-II error is equivalent to generating False Negatives Null hypothesis is true Reject Null hypothesis Null hypothesis is false Type-I error / FP Correct decision / TP ReferAccept Nullin my first presentation for/ its equivalent in diagnostic tests to slide #8 Correct rejection TN Type-II error / FN hypothesis To view more presentations and articles, visit www.eyenirvaan.com
- 25. Example of Type-I and Type-II Errors The radio engineer during WW II receives a crackling sound over his transmitter. Is this signal from the enemy or is it unwanted noise? Null hypothesis: The sound received over the transmitter is just noise Alternate hypothesis: The sound received over the transmitter is not noise True Signal Just Noise Interpretation as Signal Correctly rejecting null hypothesis False rejecting null hypothesis / Type-I error Interpretation as Noise Falsely accepting null hypothesis / Type-II error Correctly accepting the null hypothesis
- 26. Example for Type-II error What is the difference in macular thickness of eyes with AMD compared to normals, as detected using OCT imaging? •Null hypothesis: There is no difference in macular thickness between normal eyes and eyes with AMD •Alternate hypothesis: The macula in AMD patients is >50μ thicker than it is in normal eyes •Mean + 1SD macular thickness in normal eyes: 200μ + 50μ •Mean + 1SD macular thickness in AMD eyes: 230μ + 55μ •Based on these results, you have accepted the null hypothesis To view more presentations and articles, visit www.eyenirvaan.com
- 27. Example for Type-II error • Repeatability of the OCT: 80μ • The test that you have used does not have the resolution to determine the difference you are expecting • The mean difference in macular thickness between normal and AMD eyes is 110μ using a gold-standard test • An Type-II error is therefore made, in erroneously accepting the Null hypothesis To view more presentations and articles, visit www.eyenirvaan.com
- 28. Thank You! To view more presentations and articles, visit www.eyenirvaan.com