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The document discusses hypothesis testing and outlines the key steps in the hypothesis testing process: 1) Formulating the null and alternative hypotheses about a population parameter. The null hypothesis is tested while the alternative is accepted if the null is rejected. 2) Determining the significance level and critical value based on this level which establishes the boundary for rejecting the null hypothesis. 3) Selecting a sample, calculating the test statistic and comparing it to the critical value to determine whether to reject or fail to reject the null hypothesis. 4) Hypothesis tests can be one-tailed, focusing rejection in one tail, or two-tailed, splitting rejection between both tails. Steps are generally the same but null and alternatives differ.

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Hypothesis testing

This document discusses hypothesis testing, which involves drawing inferences about a population based on a sample from that population. It outlines the key elements of a hypothesis test, including the null and alternative hypotheses, test statistics, critical regions, significance levels, critical values, and p-values. Type I and Type II errors are explained, where a Type I error involves rejecting the null hypothesis when it is true, and a Type II error involves failing to reject the null when it is false. The power of a hypothesis test is defined as the probability of correctly rejecting the null hypothesis when it is false. Controlling type I and II errors involves considering the significance level, sample size, and population parameters in the null and alternative hypotheses.

Testing of Hypothesis, p-value, Gaussian distribution, null hypothesis

This document provides an overview of key concepts in statistical hypothesis testing. It defines what a hypothesis is, the different types of hypotheses (null, alternative, one-tailed, two-tailed), and statistical terms used in hypothesis testing like test statistics, critical regions, significance levels, critical values, type I and type II errors. It also explains the decision making process in hypothesis testing, such as rejecting or failing to reject the null hypothesis based on whether the test statistic falls within the critical region or if the p-value is less than the significance level.

Unit 4 Tests of Significance

Tests of significance are statistical methods used to assess evidence for or against claims based on sample data about a population. Every test of significance involves a null hypothesis (H0) and an alternative hypothesis (Ha). H0 represents the theory being tested, while Ha represents what would be concluded if H0 is rejected. A test statistic is computed and compared to a critical value to either reject or fail to reject H0. Type I and Type II errors can occur. Steps in hypothesis testing include stating hypotheses, selecting a significance level and test, determining decision rules, computing statistics, and interpreting the decision. Hypothesis tests are used to answer questions about differences in groups or claims about populations.

Hypothesis Testing

This document outlines the key steps and concepts involved in hypothesis testing. It discusses identifying the population and assumptions, stating the null and research hypotheses, determining critical values, calculating test statistics, making conclusions, and defining type I and type II errors. It also covers interval estimates, effect sizes, and statistical power.

Thesigntest

The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.

20200519073328de6dca404c.pdfkshhjejhehdhd

This document discusses hypothesis testing and various statistical tests used for hypothesis testing including t-tests, z-tests, chi-square tests, and ANOVA. It provides details on the general steps for conducting hypothesis testing including setting up the null and alternative hypotheses, collecting and analyzing sample data, and making a decision to reject or fail to reject the null hypothesis. It also discusses types of errors, required distributions, test statistics, p-values and choosing parametric or non-parametric tests based on the characteristics of the data.

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.

hypothesis.pptx

Hypothesis testing involves setting up a null hypothesis and alternative hypothesis, determining a significance level, calculating a test statistic, identifying the critical region, computing the test statistic value based on a sample, and making a decision to reject or fail to reject the null hypothesis. The z-test is used when the sample size is large and the population standard deviation is known, while the t-test is used for small samples when the population standard deviation is unknown. Both tests involve calculating a test statistic and comparing it to critical values to determine if there is sufficient evidence to reject the null hypothesis. Limitations include that the tests only indicate differences and not the reasons for them, and inferences are based on probabilities rather than certainty.

Hypothesis testing

This document discusses hypothesis testing, which involves drawing inferences about a population based on a sample from that population. It outlines the key elements of a hypothesis test, including the null and alternative hypotheses, test statistics, critical regions, significance levels, critical values, and p-values. Type I and Type II errors are explained, where a Type I error involves rejecting the null hypothesis when it is true, and a Type II error involves failing to reject the null when it is false. The power of a hypothesis test is defined as the probability of correctly rejecting the null hypothesis when it is false. Controlling type I and II errors involves considering the significance level, sample size, and population parameters in the null and alternative hypotheses.

Testing of Hypothesis, p-value, Gaussian distribution, null hypothesis

This document provides an overview of key concepts in statistical hypothesis testing. It defines what a hypothesis is, the different types of hypotheses (null, alternative, one-tailed, two-tailed), and statistical terms used in hypothesis testing like test statistics, critical regions, significance levels, critical values, type I and type II errors. It also explains the decision making process in hypothesis testing, such as rejecting or failing to reject the null hypothesis based on whether the test statistic falls within the critical region or if the p-value is less than the significance level.

Unit 4 Tests of Significance

Tests of significance are statistical methods used to assess evidence for or against claims based on sample data about a population. Every test of significance involves a null hypothesis (H0) and an alternative hypothesis (Ha). H0 represents the theory being tested, while Ha represents what would be concluded if H0 is rejected. A test statistic is computed and compared to a critical value to either reject or fail to reject H0. Type I and Type II errors can occur. Steps in hypothesis testing include stating hypotheses, selecting a significance level and test, determining decision rules, computing statistics, and interpreting the decision. Hypothesis tests are used to answer questions about differences in groups or claims about populations.

Hypothesis Testing

This document outlines the key steps and concepts involved in hypothesis testing. It discusses identifying the population and assumptions, stating the null and research hypotheses, determining critical values, calculating test statistics, making conclusions, and defining type I and type II errors. It also covers interval estimates, effect sizes, and statistical power.

Thesigntest

The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.

20200519073328de6dca404c.pdfkshhjejhehdhd

This document discusses hypothesis testing and various statistical tests used for hypothesis testing including t-tests, z-tests, chi-square tests, and ANOVA. It provides details on the general steps for conducting hypothesis testing including setting up the null and alternative hypotheses, collecting and analyzing sample data, and making a decision to reject or fail to reject the null hypothesis. It also discusses types of errors, required distributions, test statistics, p-values and choosing parametric or non-parametric tests based on the characteristics of the data.

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.

hypothesis.pptx

Hypothesis testing involves setting up a null hypothesis and alternative hypothesis, determining a significance level, calculating a test statistic, identifying the critical region, computing the test statistic value based on a sample, and making a decision to reject or fail to reject the null hypothesis. The z-test is used when the sample size is large and the population standard deviation is known, while the t-test is used for small samples when the population standard deviation is unknown. Both tests involve calculating a test statistic and comparing it to critical values to determine if there is sufficient evidence to reject the null hypothesis. Limitations include that the tests only indicate differences and not the reasons for them, and inferences are based on probabilities rather than certainty.

Hypothesis

The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

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.

Stat topics

This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.

Testing of hypothesis

This document discusses the process of testing hypotheses. It begins by defining hypothesis testing as a way to make decisions about population characteristics based on sample data, which involves some risk of error. The key steps are outlined as:
1) Formulating the null and alternative hypotheses, with the null hypothesis stating no difference or relationship.
2) Computing a test statistic based on the sample data and selecting a significance level, usually 5%.
3) Comparing the test statistic to critical values to either reject or fail to reject the null hypothesis.
Examples are provided to demonstrate hypothesis testing for a single mean, comparing two means, and testing a claim about population characteristics using sample data and statistics.

Inferential statistics hand out (2)

Inferential statistics allow researchers to draw conclusions about populations based on data from samples. They estimate population parameters and test hypotheses about populations that extend beyond the sample data. Hypothesis testing provides objective criteria for deciding whether to accept or reject research hypotheses as true or false based on the probability that any observed differences are due to chance. It involves selecting a test statistic, significance level, computing the test statistic, and comparing it to critical values to determine whether to reject the null hypothesis. Type I and Type II errors can occur but the significance level controls the risk of Type I errors.

Unit 3

This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.

Testing of Hypothesis (1) (1).pptx

1. Hypothesis testing involves stating a null hypothesis (H0) and alternative hypothesis (H1) about population parameters, selecting a test statistic, specifying a significance level (α), and establishing a critical region.
2. The null hypothesis (H0) presumes no difference between sample statistics and parameter values. The alternative hypothesis (H1) is complementary to the null hypothesis.
3. The test statistic is compared to the critical value, and the null hypothesis is accepted if the test statistic is less than the critical value, rejected otherwise.

Basic of Statistical Inference Part-IV: An Overview of Hypothesis Testing

The fourth part of the basic of statistical inference series puts its focus on discussing the concept of hypothesis testing explaining all the nuances.

Ds vs Is discuss 3.1

This document discusses descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, and summary statistics like the mean, median, mode, variance, and standard deviation. Inferential statistics allow generalization from samples to populations through hypothesis testing, where the null hypothesis is tested against the alternative hypothesis. Type I and type II errors are possible, and significance tests control the probability of type I errors through the alpha level while power analysis aims to reduce type II errors. Common inferential tests mentioned include t-tests, ANOVA, and meta-analysis.

Elements of inferential statistics

1. The document discusses key concepts in inferential statistics including point estimation, interval estimation, hypothesis testing, types of errors, p-values, power, and one-tailed and two-tailed tests.
2. It explains that inferential statistics allows generalization from a sample to a population and includes estimation of parameters and hypothesis testing.
3. Common statistical techniques covered are confidence intervals, which provide a range of values that likely contain the true population parameter, and hypothesis testing, which evaluates theories about populations.

hypothesis_testing-ch9-39-14402.pdf

1. The document discusses the basic principles of hypothesis testing, including stating the null and alternative hypotheses, selecting a significance level, choosing a test statistic, determining critical values, and making a decision to reject or fail to reject the null hypothesis.
2. It outlines the five steps of hypothesis testing: state hypotheses, select significance level, select test statistic, determine critical value, and make a decision.
3. Key terms discussed include type I and type II errors, significance levels, critical values, test statistics such as z and t, and the decision to reject or fail to reject the null hypothesis.

RM&IPR M4.pdfResearch Methodolgy & Intellectual Property Rights Series 4

This PPT is prepared for VTU-Karnataka, Mtech/PhD Research Methodology syllabus based on C.R. Kothari, Gaurav Garg, Research Methodology: Methods and Techniques, New
Age International, 4th Edition, 2018

Testing of hypothesis

The document discusses testing of hypotheses. It defines a hypothesis as a tentative prediction about the relationship between variables. Good hypotheses are precise, testable, and consistent with known facts. Hypothesis testing involves formulating a null hypothesis (Ho) and an alternative hypothesis (H1). A significance level such as 5% is chosen. If the test statistic falls within the critical region, Ho is rejected. Type I error rejects a true Ho, while Type II error accepts a false Ho. Power refers to correctly rejecting a false Ho. The testing process determines test statistics, critical regions, and interprets results to draw conclusions.

LOGIC OF HYPOTHESIS TESTING.pptx

Hypothesis Testing and its process which includes the following steps:
1.Formulation of a null hypothesis (H0) and an alternative hypothesis (Ha).
2. Determination the level of significance (α)
3. Choosing a test statistic and calculate its value.
4. Comparison between the test statistic and the critical value.
5. Making a decision and interpret the results.
This is a summary of the whole process along with easy definitions of the associated terms.

Descriptive And Inferential Statistics for Nursing Research

This document provides an overview of descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, measures of central tendency, and measures of variability. Inferential statistics allow generalization from samples to populations through hypothesis testing, which involves specifying a null hypothesis and alternative hypothesis. Statistical significance is determined by calculating a p-value and rejecting the null hypothesis if the p-value is less than a predetermined alpha level, typically 0.05. Type I and type II errors can occur in hypothesis testing.

250Lec5INFERENTIAL STATISTICS FOR RESEARC

This document provides an overview of descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, measures of central tendency, and measures of variability. Inferential statistics allow generalization from samples to populations through hypothesis testing, which involves specifying a null hypothesis and alternative hypothesis. Statistical significance is determined by calculating a p-value and comparing it to the significance level alpha to either reject or fail to reject the null hypothesis, with Type I and Type II errors a possibility. Common inferential tests include t-tests, ANOVAs, and meta-analyses.

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.

Testing Of Hypothesis

Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.

Basics of Hypothesis testing for Pharmacy

This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data

BIBLIOGRAPHY.pptx

The bibliography provides a clear and complete description of all sources used to prepare the report. It is organized alphabetically by author's surname. Each entry includes the author's surname and initials, year of publication in parentheses, title of source either underlined or in italics, place of publication, publisher or journal name, volume and page numbers. The sample bibliography lists several journal articles and books on topics related to dairy farming, milk production and marketing in India.

BREEDING METHODS AND ACHEIVEMENT IN VEGETATIVELY PROPOGATED CROPS.pptx

This document summarizes breeding methods and achievements in vegetatively propagated crops such as potato, sweet potato, cassava, colocasia, elephant foot yam, and artichokes. It provides details on the family, chromosome number, and origin of each crop. The main propagation methods covered are hybridization, backcrossing, tuber propagation for potato, stem cuttings and tuber propagation for sweet potato, cassava cuttings, colocasia tuber division, elephant foot yam offsets, and seed, offshoots or root crown division for artichokes.

Hypothesis

The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

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.

Stat topics

This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.

Testing of hypothesis

This document discusses the process of testing hypotheses. It begins by defining hypothesis testing as a way to make decisions about population characteristics based on sample data, which involves some risk of error. The key steps are outlined as:
1) Formulating the null and alternative hypotheses, with the null hypothesis stating no difference or relationship.
2) Computing a test statistic based on the sample data and selecting a significance level, usually 5%.
3) Comparing the test statistic to critical values to either reject or fail to reject the null hypothesis.
Examples are provided to demonstrate hypothesis testing for a single mean, comparing two means, and testing a claim about population characteristics using sample data and statistics.

Inferential statistics hand out (2)

Inferential statistics allow researchers to draw conclusions about populations based on data from samples. They estimate population parameters and test hypotheses about populations that extend beyond the sample data. Hypothesis testing provides objective criteria for deciding whether to accept or reject research hypotheses as true or false based on the probability that any observed differences are due to chance. It involves selecting a test statistic, significance level, computing the test statistic, and comparing it to critical values to determine whether to reject the null hypothesis. Type I and Type II errors can occur but the significance level controls the risk of Type I errors.

Unit 3

This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.

Testing of Hypothesis (1) (1).pptx

1. Hypothesis testing involves stating a null hypothesis (H0) and alternative hypothesis (H1) about population parameters, selecting a test statistic, specifying a significance level (α), and establishing a critical region.
2. The null hypothesis (H0) presumes no difference between sample statistics and parameter values. The alternative hypothesis (H1) is complementary to the null hypothesis.
3. The test statistic is compared to the critical value, and the null hypothesis is accepted if the test statistic is less than the critical value, rejected otherwise.

Basic of Statistical Inference Part-IV: An Overview of Hypothesis Testing

The fourth part of the basic of statistical inference series puts its focus on discussing the concept of hypothesis testing explaining all the nuances.

Ds vs Is discuss 3.1

This document discusses descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, and summary statistics like the mean, median, mode, variance, and standard deviation. Inferential statistics allow generalization from samples to populations through hypothesis testing, where the null hypothesis is tested against the alternative hypothesis. Type I and type II errors are possible, and significance tests control the probability of type I errors through the alpha level while power analysis aims to reduce type II errors. Common inferential tests mentioned include t-tests, ANOVA, and meta-analysis.

Elements of inferential statistics

1. The document discusses key concepts in inferential statistics including point estimation, interval estimation, hypothesis testing, types of errors, p-values, power, and one-tailed and two-tailed tests.
2. It explains that inferential statistics allows generalization from a sample to a population and includes estimation of parameters and hypothesis testing.
3. Common statistical techniques covered are confidence intervals, which provide a range of values that likely contain the true population parameter, and hypothesis testing, which evaluates theories about populations.

hypothesis_testing-ch9-39-14402.pdf

1. The document discusses the basic principles of hypothesis testing, including stating the null and alternative hypotheses, selecting a significance level, choosing a test statistic, determining critical values, and making a decision to reject or fail to reject the null hypothesis.
2. It outlines the five steps of hypothesis testing: state hypotheses, select significance level, select test statistic, determine critical value, and make a decision.
3. Key terms discussed include type I and type II errors, significance levels, critical values, test statistics such as z and t, and the decision to reject or fail to reject the null hypothesis.

RM&IPR M4.pdfResearch Methodolgy & Intellectual Property Rights Series 4

This PPT is prepared for VTU-Karnataka, Mtech/PhD Research Methodology syllabus based on C.R. Kothari, Gaurav Garg, Research Methodology: Methods and Techniques, New
Age International, 4th Edition, 2018

Testing of hypothesis

The document discusses testing of hypotheses. It defines a hypothesis as a tentative prediction about the relationship between variables. Good hypotheses are precise, testable, and consistent with known facts. Hypothesis testing involves formulating a null hypothesis (Ho) and an alternative hypothesis (H1). A significance level such as 5% is chosen. If the test statistic falls within the critical region, Ho is rejected. Type I error rejects a true Ho, while Type II error accepts a false Ho. Power refers to correctly rejecting a false Ho. The testing process determines test statistics, critical regions, and interprets results to draw conclusions.

LOGIC OF HYPOTHESIS TESTING.pptx

Hypothesis Testing and its process which includes the following steps:
1.Formulation of a null hypothesis (H0) and an alternative hypothesis (Ha).
2. Determination the level of significance (α)
3. Choosing a test statistic and calculate its value.
4. Comparison between the test statistic and the critical value.
5. Making a decision and interpret the results.
This is a summary of the whole process along with easy definitions of the associated terms.

Descriptive And Inferential Statistics for Nursing Research

This document provides an overview of descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, measures of central tendency, and measures of variability. Inferential statistics allow generalization from samples to populations through hypothesis testing, which involves specifying a null hypothesis and alternative hypothesis. Statistical significance is determined by calculating a p-value and rejecting the null hypothesis if the p-value is less than a predetermined alpha level, typically 0.05. Type I and type II errors can occur in hypothesis testing.

250Lec5INFERENTIAL STATISTICS FOR RESEARC

This document provides an overview of descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, measures of central tendency, and measures of variability. Inferential statistics allow generalization from samples to populations through hypothesis testing, which involves specifying a null hypothesis and alternative hypothesis. Statistical significance is determined by calculating a p-value and comparing it to the significance level alpha to either reject or fail to reject the null hypothesis, with Type I and Type II errors a possibility. Common inferential tests include t-tests, ANOVAs, and meta-analyses.

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.

Testing Of Hypothesis

Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.

Basics of Hypothesis testing for Pharmacy

This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data

Hypothesis

Hypothesis

Presentation chi-square test & Anova

Presentation chi-square test & Anova

Testing of Hypothesis.pptx

Testing of Hypothesis.pptx

Stat topics

Stat topics

Testing of hypothesis

Testing of hypothesis

Inferential statistics hand out (2)

Inferential statistics hand out (2)

Unit 3

Unit 3

Testing of Hypothesis (1) (1).pptx

Testing of Hypothesis (1) (1).pptx

Basic of Statistical Inference Part-IV: An Overview of Hypothesis Testing

Basic of Statistical Inference Part-IV: An Overview of Hypothesis Testing

Ds vs Is discuss 3.1

Ds vs Is discuss 3.1

Elements of inferential statistics

Elements of inferential statistics

hypothesis_testing-ch9-39-14402.pdf

hypothesis_testing-ch9-39-14402.pdf

RM&IPR M4.pdfResearch Methodolgy & Intellectual Property Rights Series 4

RM&IPR M4.pdfResearch Methodolgy & Intellectual Property Rights Series 4

Testing of hypothesis

Testing of hypothesis

LOGIC OF HYPOTHESIS TESTING.pptx

LOGIC OF HYPOTHESIS TESTING.pptx

Descriptive And Inferential Statistics for Nursing Research

Descriptive And Inferential Statistics for Nursing Research

250Lec5INFERENTIAL STATISTICS FOR RESEARC

250Lec5INFERENTIAL STATISTICS FOR RESEARC

Hypothesis Testing

Hypothesis Testing

Testing Of Hypothesis

Testing Of Hypothesis

Basics of Hypothesis testing for Pharmacy

Basics of Hypothesis testing for Pharmacy

BIBLIOGRAPHY.pptx

The bibliography provides a clear and complete description of all sources used to prepare the report. It is organized alphabetically by author's surname. Each entry includes the author's surname and initials, year of publication in parentheses, title of source either underlined or in italics, place of publication, publisher or journal name, volume and page numbers. The sample bibliography lists several journal articles and books on topics related to dairy farming, milk production and marketing in India.

BREEDING METHODS AND ACHEIVEMENT IN VEGETATIVELY PROPOGATED CROPS.pptx

This document summarizes breeding methods and achievements in vegetatively propagated crops such as potato, sweet potato, cassava, colocasia, elephant foot yam, and artichokes. It provides details on the family, chromosome number, and origin of each crop. The main propagation methods covered are hybridization, backcrossing, tuber propagation for potato, stem cuttings and tuber propagation for sweet potato, cassava cuttings, colocasia tuber division, elephant foot yam offsets, and seed, offshoots or root crown division for artichokes.

statement of the problem.pptx

This document provides a statement and justification of a problem related to conducting economic analysis of guava marketing and processing in Kaushambi District, Uttar Pradesh, India. The problem statement identifies issues with guava cultivation such as declining yields and shorter orchard lifespans. It also notes a lack of research on guava marketing and processing. The purpose section explains that the problem statement should describe the current problem without defining solutions. The justification section argues that the study is needed to provide guidance to farmers, businesses, and institutions on the costs and prospects of guava marketing and processing to support continued production and investment.

Overview of research methodology.pptx

The document discusses key aspects of research methodology. It defines research as a systematic, rigorous process of inquiry aimed at discovering and interpreting facts. The characteristics of good research are that it is controlled, empirical, and can withstand critical analysis. Research involves defining a problem, collecting and analyzing data, and reporting findings. Key steps in the research process are problem identification, literature review, design, data collection and analysis, interpretation and reporting conclusions. The summary provides a high-level overview of the main topics and process discussed in the document.

onionavisha-170706145613 (1).pptx

The document provides information on the production technology of onion. It discusses the botanical classification of onion, describes common varieties grown in India along with their characteristics, and provides production statistics. Some key points include:
- Onion is an important commercial vegetable crop grown worldwide, with India as the second largest producer.
- Common varieties described include red varieties like Pusa Red, Arka Kalyan, Bhima Raj; and white varieties like Pusa White, Bhima Shweta. Varieties differ in shape, color, pungency, and maturity duration.
- Leading onion producing states in India are Gujarat, Punjab and Maharashtra. Highest productivity is seen in Gujarat

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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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Agenda
● What is event processing in MuleSoft?
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For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
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Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw

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- 2. Hypothesis An idea or explanation for something that is based on known facts but not yet has been proved
- 3. Formulating the Hypothesis The null hypothesis is a statement about the population value that will be tested. The null hypothesis will be rejected only if the sample data provide substantial contradictory evidence.
- 4. Formulating the Hypothesis The alternative hypothesis is the hypothesis that includes all population values not covered by the null hypothesis. The alternative hypothesis is deemed to be true if the null hypothesis is rejected.
- 5. Formulating the Hypothesis The research hypothesis is the hypothesis the decision maker attempts to demonstrate to be true. Since this is the hypothesis deemed to be the most important to the decision maker, it will not be declared true unless the sample data strongly indicates that it is true.
- 6. Types of Statistical Errors Type I Error - This type of statistical error occurs when the null hypothesis is true and is rejected. Type II Error - This type of statistical error occurs when the null hypothesis is false and is not rejected.
- 7. The power of a test is the probability (1 - ) of rejecting the null hypothesis when it is false and should be rejected. Although is unknown, it is related to . An extremely low value of (e.g., = 0.001) will result in intolerably high errors. So it is necessary to balance the two types of errors. Power of a Test
- 8. Establishing the Decision Rule The critical value is the value of a statistic corresponding to a given significance level. This cutoff value determines the boundary between the samples resulting in a test statistic that leads to rejecting the null hypothesis and those that lead to a decision not to reject the null hypothesis.
- 9. Establishing the Decision Rule The significance level is the maximum probability of committing a Type I statistical error. The probability is denoted by the symbol .
- 10. Reject H0 x x 25 Do not reject H0 Sampling Distribution Maximum probability of committing a Type I error = Establishing the Decision Rule
- 11. x z 25 Rejection region = 0.10 28 . 1 z 0 From the standard normal table 28 . 1 10 . 0 z Then 28 . 1 z 0.5 0.4 Establishing the Critical Value as a z -Value
- 12. ? x z 25 Rejection region = 0.10 28 . 1 z 0 0.5 0.4 Example of Determining the Critical Value 64 3 n x x for Solving 64 3 28 . 1 25 n z x 48 . 25 x
- 13. Establishing the Decision Rule The test statistic is a function of the sampled observations that provides a basis for testing a statistical hypothesis.
- 14. Summary of Hypothesis Testing Process The hypothesis testing process can be summarized in 6 steps: Determine the null hypothesis and the alternative hypothesis. Determine the desired significance level (). Define the test method and sample size and determine a critical value. Select the sample, calculate sample mean, and calculate the z-value or p-value. Establish a decision rule comparing the sample statistic with the critical value. Reach a conclusion regarding the null hypothesis.
- 15. One-Tailed Hypothesis Tests A one-tailed hypothesis test is a test in which the entire rejection region is located in one tail of the test statistic’s distribution.
- 16. Two-Tailed Hypothesis Tests A two-tailed hypothesis test is a test in which the rejection region is split between the two tails of the test statistic’s distribution.
- 17. Hypothesis Tests for Two Population Variances HYPOTHESIS TESTING STEPS Formulate the null and alternative hypotheses in terms of the population parameter of interest. Determine the level of significance. Determine the critical value of the test statistic. Select the sample and compute the test statistic. Compare the calculated test statistic to the critical value and reach a conclusion.
- 18. Independent Samples Independent samples are those samples selected from two or more populations in such a way that the occurrence of values in one sample have no influence on the probability of the occurrence of values in the other sample(s).
- 19. Thank you