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This presentation educates you about T-Test, Key takeways, Assumptions for Performing a t-test, Types of t-tests, One sample t-test, Independent two-sample t-test and Paired sample t-test. For more topics Stay tuned with Learnbay
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Hypothesis and Hypothesis Testing
Hypothesis testing involves proposing and testing hypotheses, or predictions, about relationships between variables. There are four main types of hypotheses: null, alternative, directional, and non-directional. The null hypothesis proposes no relationship between variables, while the alternative hypothesis contradicts the null. Directional hypotheses predict the nature of a relationship, while non-directional hypotheses do not. Common statistical tests used for hypothesis testing include the z-test, t-test, chi-square test, and F-test. Hypothesis testing is a crucial part of the scientific method for assessing theories through empirical observation.
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Degree of freedom refers to the number of independent pieces of information used to calculate a statistic. In an example where heights of 5 students were measured, taking a single sample of 1 student's height of 8 feet to calculate variance would have 1 degree of freedom. Taking 2 independent samples of heights 8 feet and 5 feet would have 2 degrees of freedom. When estimating the population mean from samples to then calculate variance, the degrees of freedom is the number of samples minus 1, as the values are not fully independent after estimating the mean.
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This document discusses hypothesis testing, including:
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This document provides an overview of hypotheses testing in research. It defines a hypothesis as an explanation or proposition that can be tested scientifically. The main points covered are:
1. The general procedure for hypothesis testing involves making formal statements of the null and alternative hypotheses, selecting a significance level, choosing a statistical distribution, collecting a random sample, calculating probabilities, and comparing probabilities to determine whether to reject or fail to reject the null hypothesis.
2. There are two types of hypotheses tests - one-tailed and two-tailed. A one-tailed test has one rejection region while a two-tailed test has two rejection regions, one in each tail.
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3) The two types of errors in hypothesis testing are discussed. Hypothesis tests can result in correct decisions or two types of errors when the null hypothesis is true or false.
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This document provides an overview of hypotheses testing in research. It defines a hypothesis as an explanation or proposition that can be tested scientifically. The main points covered are:
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The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
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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.
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This document discusses hypotheses, including their characteristics, criteria for construction, testing approaches, and types of errors. It defines a hypothesis as a tentative explanation for behaviors or events that can be scientifically tested. Key points include:
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- The null hypothesis is what is being tested, while the alternative is what may be accepted if the null is rejected.
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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,
T test
A t-test was conducted to analyze order data from a salesman over 9 days to determine if the average order was different than 65. The orders were 70, 66, 69, 65, 69, 70, 71, 64, 63, and 68. With a 5% significance level of 1.8333, the t-test was used to examine whether the mean order of the month was different than 65.
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The document discusses different types of t-tests, including one-sample, independent two-sample, and paired sample t-tests. It provides an example of a telecom company measuring average customer service times at two stores to determine if there is a significant difference. The key assumptions for t-tests are that the data is continuous, randomly selected, normally distributed, and has equal variances for independent two-sample tests. Steps for performing a one-sample t-test to compare a sample mean to a theoretical value are shown.
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This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
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2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
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The document provides an overview of hypothesis testing. It begins by defining a hypothesis test and its purpose of ruling out chance as an explanation for research study results. It then outlines the logic and steps of a hypothesis test: 1) stating hypotheses, 2) setting decision criteria, 3) collecting data, 4) making a decision. Key concepts discussed include type I and type II errors, statistical significance, test statistics like the z-score, and assumptions of hypothesis testing. Factors that can influence a hypothesis test like effect size, sample size, and alpha level are also covered.
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The document outlines the steps to perform the Wilcoxon Signed Rank Test to compare two related samples:
1) Obtain the differences between paired values in two samples and rank the absolute differences.
2) Assign ranks to positive and negative differences and calculate the sum of ranks.
3) Compare the smaller sum of ranks (T) to critical values to determine if the null hypothesis that the samples are identical can be rejected.
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The document provides information about various statistical hypothesis tests that can be used to analyze data and test if process improvements have resulted in significant changes. It discusses one proportion tests, two proportions tests, one-variance tests, two-variances tests, and how to determine which test to use based on the type of data and questions being asked. Examples are also provided of applying these tests using Minitab software to analyze sample data and test hypotheses about changes between before and after process improvement situations. The document aims to help determine the appropriate statistical tests for validating improvements in processes.
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1) The document presents information on different types of t-tests including the single sample t-test, independent sample t-test, and dependent/paired sample t-test. Equations and examples are provided for each.
2) The single sample t-test compares the mean of a sample to a hypothesized population mean. The independent t-test compares the means of two independent samples. The dependent t-test compares the means of two related samples, such as pre-and post-test scores.
3) A z-test is also discussed and compared to t-tests. The z-test is used when the population standard deviation is known and sample sizes are large, while t-tests are used
Sampling distribution
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- The central limit theorem states that sampling distributions of sample means will be approximately normally distributed regardless of the population distribution, if the sample size is large.
- Standard error measures the amount of variability in values of a sample statistic across different samples. It is used to construct confidence intervals for population parameters.
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This document provides information about the Kruskal-Wallis H test, a non-parametric method for testing whether samples originate from the same distribution. It describes how the Kruskal-Wallis test is a generalization of the Mann-Whitney U test that allows comparison of more than two independent groups. The test works by ranking all data from lowest to highest and then summing the ranks for each group to calculate the test statistic H, which is compared to a chi-squared distribution to determine whether to reject or fail to reject the null hypothesis that all population medians are equal.
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Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
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Testing of hypothesis
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the hypothesized parameter value. If the sample value differs significantly from the hypothesized value based on a predetermined significance level, then the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample value is greater than or less than the hypothesized value, or two-tailed, testing if the sample value is significantly different from the hypothesized value.
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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.
Parametric Statistical tests
In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Non parametric methods
This document provides an introduction and overview of non-parametric statistical methods, including ranks and the median, Wilcoxon signed rank test, Mann-Whitney test, and Spearman's rank correlation coefficient. It defines what non-parametric tests are, discusses their advantages over parametric tests in situations where data is not normally distributed, and provides examples of calculating and interpreting several non-parametric tests in SPSS.
Hypothesis testing
This document discusses hypotheses, including their characteristics, criteria for construction, testing approaches, and types of errors. It defines a hypothesis as a tentative explanation for behaviors or events that can be scientifically tested. Key points include:
- Hypotheses must be clear, precise, testable and specify the relationship between variables.
- The null hypothesis is what is being tested, while the alternative is what may be accepted if the null is rejected.
- Tests can be two-tailed, testing in both directions, or one-tailed, testing in one specified direction.
- Type I error occurs when a true null hypothesis is rejected, while Type II error is accepting a false null hypothesis.
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Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between sample means to the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and control groups.
students_t_test.ppt
Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between two sample means over the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and untreated groups.
Medical Statistics.ppt
This document provides information about medical statistics including what statistics are, how they are used in medicine, and some key statistical concepts. It discusses that statistics is the study of collecting, organizing, summarizing, presenting, and analyzing data. Medical statistics specifically deals with applying these statistical methods to medicine and health sciences areas like epidemiology, public health, and clinical research. It also overview some common statistical analyses like descriptive versus inferential statistics, populations and samples, variables and data types, and some statistical notations.
Parametric & non-parametric
This document provides an overview of parametric and nonparametric statistical methods. It defines key concepts like standard error, degrees of freedom, critical values, and one-tailed versus two-tailed hypotheses. Common parametric tests discussed include t-tests, ANOVA, ANCOVA, and MANOVA. Nonparametric tests covered are chi-square, Mann-Whitney U, Kruskal-Wallis, and Friedman. The document explains when to use parametric versus nonparametric methods and how measures like effect size can quantify the strength of relationships found.
t Test- Thiyagu
This presentation describes the concept of One Sample t-test, Independent Sample t-test and Paired Sample t-test. This presentation also deals about the procedure to do the t-test through SPSS.
t-test vs ANOVA
T-test and ANOVA are statistical techniques used to test hypotheses and compare population means. The t-test is used to compare the means of two samples or groups, while ANOVA can compare the means of more than two groups. Specifically, the t-test examines whether two sample means are significantly different and assumes a normal distribution and unknown standard deviation. ANOVA compares three or more population means by assessing variation within and between groups, and assumes samples are from normally distributed populations with equal variances. Researchers should use a t-test when comparing only two means and ANOVA when comparing more than two means to avoid increasing the chances of a Type I error.
1. complete stats notes
1) Statistics is the science of collecting, analyzing, and drawing conclusions from data. It is used to understand populations based on samples since directly measuring entire populations is often impossible.
2) There are two main types of data: qualitative data which relates to descriptive characteristics, and quantitative data which can be expressed numerically. Common statistical analyses include calculating the mean, standard deviation, and using t-tests, ANOVA, correlation, and chi-squared tests.
3) Statistical analyses allow researchers to determine uncertainties in measurements, compare groups, identify relationships between variables, and assess whether observed differences are likely due to chance or a factor being studied. Key concepts include null and alternative hypotheses, p-values, and effect size.
Statistics
This document provides an overview of common statistical tests used in dentistry research. It first describes descriptive statistics like measures of central tendency, dispersion, position, and outliers. It then discusses inferential statistics including parametric tests like t-tests and ANOVA that assume normal distributions, and non-parametric tests that make fewer assumptions. Specific parametric tests covered are the independent and paired t-tests and ANOVA. Non-parametric tests discussed include the chi-square, Wilcoxon, Mann-Whitney U, and Kruskal-Wallis tests. The document also briefly explains correlation/regression and measures of effect size like relative risk and odds ratios.
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T test
- 1. Swipe T-Test
- 2. A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. It is mostly used when the data sets, like the data set recorded as the outcome from flipping a coin 100 times, would follow a normal distribution and may have unknown variances. T-Test
- 3. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population. A t-test looks at the t-statistic, the t-distribution values, and the degrees of freedom to determine the statistical significance. To conduct a test with three or more means, one must use an analysis of variance. T-Test
- 4. A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics. Key takeways
- 5. Calculating a t-test requires three key data values. They include the difference between the mean values from each data set (called the mean difference), the standard deviation of each group, and the number of data values of each group. There are several different types of t-test that can be performed depending on the data and type of analysis required.
- 6. The data should follow a continuous or ordinal scale (the IQ test scores of students, for example) The observations in the data should be randomly selected The data should resemble a bell-shaped curve when we plot it, i.e., it should be normally distributed. You can refer to this article to get a better understanding of the normal distribution Large sample size should be taken for the data to approach a normal distribution (although t-test is essential for small samples as their distributions are non-normal) Variances among the groups should be equal (for independent two-sample t-test) Assumptions for Performing a t-test
- 7. One sample t-test Independent two-sample t-test Paired sample t-test Types of t-tests
- 8. The one-sample t-test is a statistical hypothesis test used to determine whether an unknown population mean is different from a specific value. One sample t-test
- 9. The two-sample t-test (also known as the independent samples t-test) is a method used to test whether the unknown population means of two groups are equal or not. Independent two-sample t-test
- 10. The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations Paired sample t-test
- 11. Chi-square Test Non-Probability methods Sentimental Analysis Stay Tuned with Topics for next Post