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This document summarizes a lecture on non-parametric statistics. It begins by distinguishing between parametric and non-parametric statistics, noting that parametric methods assume a particular distribution and population parameters while non-parametric methods do not. Examples of non-parametric equivalents to common parametric tests are then provided, including the Wilcoxon signed-rank test for paired samples and the Mann-Whitney U test for independent samples. These are demonstrated on wine tasting and ranking data examples.
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DIstinguish between Parametric vs nonparametric test
This document summarizes parametric and nonparametric tests. Parametric tests make assumptions about the population based on known parameters, while nonparametric tests make no assumptions about the population. Some examples of parametric tests provided are t-test, F-test, z-test, and ANOVA, while examples of nonparametric tests include Mann-Whitney, rank sum test, and Kruskal-Wallis test. The key differences between parametric and nonparametric tests are that parametric tests are based on population parameters and distributions while nonparametric tests are not, and parametric tests can only be applied to variable data while nonparametric tests can be used for variable or attribute data.
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This document provides an introduction to parametric tests in statistics. It defines a parametric test as a statistical test that makes specific assumptions about the population parameter. For a test to be considered parametric, the data must meet four conditions: it must be on an interval or ratio scale, subjects must be randomly selected, and the data must be normally distributed. Examples of parametric tests include t-tests, ANOVA, and z-tests. Parametric tests are more powerful than non-parametric tests if their assumptions are met, but non-parametric tests can be used when the data does not meet parametric assumptions, such as with nominal scale data.
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.
Parametric versus non parametric test
Parametric and non-parametric tests differ in their assumptions about the population. Parametric tests assume the population is normally distributed and have equal variances, while non-parametric tests make no assumptions. Parametric tests are more powerful but require their assumptions to be met. Non-parametric tests are simpler and not affected by outliers. The document provides examples of common parametric and non-parametric tests for different study types such as comparing two or more groups or measuring the association between variables.
Parametric vs non parametric sem2 final
This document summarizes the key differences between parametric and non-parametric tests. Parametric tests make specific assumptions about the population, such as the data following a normal distribution. They are more powerful but only apply if their assumptions are met. Non-parametric tests make no assumptions about the population and can be used on nominal scale data, but are generally less powerful. The document provides examples of common parametric and non-parametric tests used for different study types such as comparing two or more groups.
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This document summarizes nonparametric hypothesis testing methods that can be used when sample sizes are small and variables are not normally distributed. It describes the sign test, which tests for differences in paired data by counting how many pairs differ, and the Mann-Whitney U test for unpaired data, which ranks all values together and tests for differences in average ranks between samples. An example compares the income of applicants for fixed-rate versus variable-rate mortgages using the Mann-Whitney U test, finding the probability of difference being due to chance is 18.3%, suggesting variable rate applicants have higher income.
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DIstinguish between Parametric vs nonparametric test
This document summarizes parametric and nonparametric tests. Parametric tests make assumptions about the population based on known parameters, while nonparametric tests make no assumptions about the population. Some examples of parametric tests provided are t-test, F-test, z-test, and ANOVA, while examples of nonparametric tests include Mann-Whitney, rank sum test, and Kruskal-Wallis test. The key differences between parametric and nonparametric tests are that parametric tests are based on population parameters and distributions while nonparametric tests are not, and parametric tests can only be applied to variable data while nonparametric tests can be used for variable or attribute data.
Parametric test
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Non parametric methods
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Parametric versus non parametric test
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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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Parametric and nonparametric procedures are two broad classifications of statistical tests. Parametric tests make assumptions about the underlying data distribution, often assuming it is normal. Nonparametric tests do not rely on such distribution assumptions. If data strongly deviate from parametric assumptions, a nonparametric test may be more appropriate to avoid incorrect conclusions. However, nonparametric tests generally have less statistical power and their results can be harder to interpret. It is important to consider the assumptions of parametric tests and whether nonparametric alternatives should be used instead.
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Non-parametric statistics is a branch of statistics that does not require data to be normally distributed. It can be used with ordinal or ranked data and does not assume a particular distribution shape or require parameters like the mean or standard deviation. Common non-parametric tests include rank sum tests like the Wilcoxon-Mann-Whitney U test and the Kruskal-Wallis H test, the chi-square test, and Spearman's rank correlation test. These tests make fewer assumptions about the underlying data distribution compared to parametric tests.
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Parametric tests make specific assumptions about the population parameters, such as the data being normally distributed. Non-parametric tests do not assume a particular distribution and instead focus on differences in medians or proportions. Common assumptions for parametric tests include normality of data, homogeneity of variances between groups, a linear relationship between variables, and independence of observations. Specific parametric tests like the t-test and ANOVA also assume random sampling from a normally distributed population and additivity of treatment effects.
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this ppt describes about descriptive and inferential statistics, levels of measurement, hypothesis testing, parametric, non parametric tests.
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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:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
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3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,
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Parametric and nonparametric procedures are two broad classifications of statistical tests. Parametric tests make assumptions about the underlying data distribution, often assuming it is normal. Nonparametric tests do not rely on such distribution assumptions. If data strongly deviate from parametric assumptions, a nonparametric test may be more appropriate to avoid incorrect conclusions. However, nonparametric tests generally have less statistical power and their results can be harder to interpret. It is important to consider the assumptions of parametric tests and whether nonparametric alternatives should be used instead.
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Non-parametric statistics is a branch of statistics that does not require data to be normally distributed. It can be used with ordinal or ranked data and does not assume a particular distribution shape or require parameters like the mean or standard deviation. Common non-parametric tests include rank sum tests like the Wilcoxon-Mann-Whitney U test and the Kruskal-Wallis H test, the chi-square test, and Spearman's rank correlation test. These tests make fewer assumptions about the underlying data distribution compared to parametric tests.
The mann whitney u test
The Mann-Whitney U Test is used to compare two independent groups on an ordinal scale. It tests the null hypothesis that there is no difference between the groups' rankings. The document provides an example comparing traditional language learning to immersion learning. Students' Spanish test scores were ranked, and the Mann-Whitney U Test found a significant difference, rejecting the null hypothesis. The immersion group had higher rankings than the traditional group, showing greater Spanish proficiency from immersion learning.
Non parametric presentation
Nonparametric tests can analyze ordinal or nominal data without assumptions about the population distribution. They include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test. SPSS examples demonstrate using the binomial test to compare a proportion to 50%, the Kolmogorov-Smirnov test to check normality, and Kruskal-Wallis to compare more than two independent groups.
Repeated anova measures ppt
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Non parametric-tests
The document discusses several non-parametric tests that can be used as alternatives to parametric tests when the assumptions of parametric tests are violated. Specifically, it discusses:
1. The sign test and one sample median test, which can be used instead of t-tests when the data is skewed or not normally distributed.
2. Mood's median test, which compares the medians of two independent samples and is the nonparametric version of a one-way ANOVA.
3. The Kruskal-Wallis test, which determines if there are differences in medians across three or more groups and is the nonparametric version of a one-way ANOVA.
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Non parametric study; Statistical approach for med student
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Parametric vs Nonparametric Tests: When to use which
Parametric vs Nonparametric Tests: When to use which
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Assumptions about parametric and non parametric tests
Deciphering the dilemma of parametric and nonparametric tests
Deciphering the dilemma of parametric and nonparametric tests
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This document provides an overview of key concepts in statistics, including:
- Population and sample, descriptive statistics like measures of central tendency and dispersion, the normal distribution, and the central limit theorem.
- Correlation, covariance, probability, conditional probability, Bayes' rule, statistical tests, random variables, and specific distributions like binomial, Poisson, and normal.
- Key terms are defined like mean, median, mode, variance, standardized normal distribution, skewness, correlation, conditional probability, hypothesis testing, and random variables.
What is a Kruskal Wallis-Test?
The Kruskal-Wallis test is a non-parametric analogue to a one-way ANOVA test used to compare differences between two or more independent groups when the dependent variable is measured on an ordinal scale or when the distribution is skewed. It works by ranking the data and estimating differences in ranks among the groups. For example, it could be used to test for differences in student preference for watching rugby (measured on a scale from strong dislike to strong like) between freshmen, sophomores, juniors, and seniors. A significant Kruskal-Wallis result should then be followed up with post-hoc non-parametric tests to determine where the differences between groups occur.
Non parametric tests
Assumptions of parametric and non-parametric tests
Testing the assumption of normality
Commonly used non-parametric tests
Applying tests in SPSS
Advantages of non-parametric tests
Limitations
Parametric and non parametric test
Statistical tests can be used to analyze data in two main ways: descriptive statistics provide an overview of data attributes, while inferential statistics assess how well data support hypotheses and generalizability. There are different types of tests for comparing means and distributions between groups, determining if differences or relationships exist in parametric or non-parametric data. The appropriate test depends on the question being asked, number of groups, and properties of the data.
Non-Parametric Tests
This document provides an overview of nonparametric tests. It defines nonparametric tests as techniques that do not rely on assumptions about the underlying data distribution. Some key points made in the document include:
- Nonparametric tests are used when the sample distribution is unknown or when there are too many variables to assume a normal distribution.
- Common nonparametric tests include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test.
- The main difference between parametric and nonparametric tests is that parametric tests make assumptions about the population distribution, while nonparametric tests do not require these assumptions and are distribution-
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This document discusses parametric and nonparametric statistics, when each should be used, and their relative power. Parametric tests make assumptions about the population that may not always be justified, while nonparametric tests do not rely on these assumptions. Some key points:
1. Parametric tests assume normal distributions and equal variances, while nonparametric tests do not require these assumptions and can be used on small sample sizes or ordinal data.
2. There are nonparametric equivalents to most common parametric tests for comparing groups or relationships between variables.
3. Advantages of nonparametric tests are they provide exact probabilities regardless of the population distribution and can be used on small sample
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The document discusses several nonparametric statistical tests:
1) The Wilcoxon signed-rank test can be used to test hypotheses about one population median with minimal assumptions about the distribution.
2) The Mann-Whitney U-test compares two independent samples and can detect differences in population medians.
3) The Kruskal-Wallis test compares more than two population medians and is a nonparametric equivalent of one-way ANOVA.
The median test
I. The median test is used to determine if two independent groups have been drawn from populations with the same median. It requires at least ordinal scale data.
II. The combined median of both groups is calculated. Scores from each group are then split based on whether they are above or below the combined median. These frequencies are entered into a 2x2 contingency table.
III. The median test statistic (chi-square) is calculated and compared to a critical value based on the significance level and degrees of freedom to determine whether to reject or fail to reject the null hypothesis that the two groups have the same median.
Statistical methods for the life sciences lb
This document discusses nonparametric statistical methods and rank tests. It begins with an introductory example comparing blood pressure readings before and after surgery using a paired t-test approach and discussing its assumptions. It then introduces nonparametric hypothesis testing as an alternative that does not rely on distributional assumptions. The document outlines what test to use based on factors like the number of groups and whether samples are independent or dependent. It provides detailed examples of the Wilcoxon rank sum test to compare two independent samples and the Kruskal-Wallis and Friedman tests for comparing more than two groups.
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The document discusses the Friedman test, a non-parametric statistical test used to detect differences in treatments across multiple test attempts. It provides information on the history, assumptions, general procedure, applications, advantages and disadvantages of the Friedman test. An example is also included to demonstrate how to perform the Friedman test and analyze the results.
Kruskal Wall Test
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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Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
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3. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables.
T12 non-parametric tests
This document discusses several nonparametric tests:
1. The Sign Test is used for paired data and makes no assumptions about the distribution of the data. It looks at the signs of differences between pairs to determine if the median difference is zero.
2. The Mann-Whitney U Test compares two independent groups and uses ranks rather than raw values. It does not assume normality or equal variance like the t-test.
3. The Kruskal-Wallis H Test compares more than two populations and ranks all measurements jointly to compare distributions using rank sums.
It also briefly outlines Spearman's Rank Correlation test, the Run Test for Randomness, and the Cox-Stuart
5. Non parametric analysis
This document outlines a lecture on non-parametric statistics. It begins by defining parametric and non-parametric tests, noting that non-parametric tests do not require assumptions of normality and can be used when data is not of sufficient quality for parametric tests. It then reviews the assumptions of common parametric t-tests and how to determine if the data violates assumptions. The document introduces several non-parametric tests: the Mann-Whitney U test for comparing two independent groups, the Wilcoxon test for comparing two related groups, and the Kruskal-Wallis H test for comparing three or more independent groups. It provides examples and outlines how to perform each test in SPSS.
Advance Statistics - Wilcoxon Signed Rank Test
The Wilcoxon signed-rank test is a non-parametric test used to compare two related samples, such as repeated measurements on a single sample, to assess whether their population mean ranks differ. It can be used as a non-parametric alternative to the paired Student's t-test when the population cannot be assumed to be normally distributed. The test involves ranking the differences between pairs of observations and comparing the sum of the ranks of the positive differences to what would be expected if there was no effect. The document provides information on the requirements, formula, and an example application of the Wilcoxon signed-rank test.
Sampling techniquesmod5
Here are the steps to solve this problem using stratified random sampling:
1. Divide the population into strata based on the barangays.
2. Calculate the sample size for each stratum proportionately based on the total sample size (1000 residents) and population size of each stratum.
3. Randomly select the calculated sample size from each stratum.
Barangay Population Proportion of sample Sample size
Mapayapa 2,000 0.2 200
Malinis 1,000 0.1 100
Mahangin 1,500 0.15 150
Mabunga 2,500 0.25 250
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The document summarizes the assumptions for various statistical tests covered in the course. It discusses how to test if the assumption of a normal distribution in the outcome variable is met, including examining histograms, Q-Q plots, and test statistics. If the normality assumption is violated, the document describes common transformation techniques, such as square root, logarithm, or reflect and square root transforms, that may help normalize the distribution.
Lecture slides stats1.13.l20.air
This document summarizes a lecture on binary logistic regression. It begins with an overview of binary logistic regression, noting that it is used to predict a binary categorical outcome variable from predictor variables that may be continuous or categorical. The second segment provides an example using data from mock jury research, with the outcome being a death penalty verdict and predictors being jurors' beliefs. Key outputs of binary logistic regression are explained such as regression coefficients, odds ratios, Wald tests, and measures of model fit and classification success.
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This document provides an overview of chi-square tests, including chi-square goodness of fit and chi-square test of independence. It uses examples from a hypothetical New York City mayoral election poll to demonstrate how to perform each test. The chi-square goodness of fit test determines if the distribution of proportions in a sample fits the expected distribution. The chi-square test of independence determines if there is a relationship between two categorical variables, like voter gender and candidate preference. Both tests use a chi-square calculation and degrees of freedom to obtain a p-value, and Cramér's V can estimate effect size.
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This document summarizes key aspects of a lecture on repeated measures ANOVA:
1) Repeated measures designs have pros like requiring fewer subjects and having more statistical power but cons like potential order effects and needing to meet extra assumptions.
2) An example compares working memory scores of 20 subjects across 4 training conditions to illustrate a repeated measures design and analysis.
3) The analysis of this example includes a repeated measures ANOVA, followed by paired t-tests and multiple post-hoc tests to compare conditions and control Type I error rates.
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This lecture discusses factorial ANOVA:
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This document summarizes a lecture on analysis of variance (ANOVA) and post-hoc tests. It begins with an overview of one-way ANOVA, including how it is used to compare more than two group means when the independent variable is categorical and the dependent variable is continuous. Assumptions of ANOVA like normality and homogeneity of variance are discussed. The document then explains how post-hoc tests allow multiple pairwise comparisons between groups following a significant ANOVA without increasing Type I error rates, comparing procedures from liberal to conservative. Worked examples on working memory training data are provided.
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This lecture covered dependent and independent t-tests for comparing sample means. It discussed when to use a z-test, single sample t-test, dependent t-test, and independent t-test. The dependent t-test is used to compare related samples, such as pre-post scores, while the independent t-test compares unrelated samples. Both tests calculate the t-value, p-value, effect size, and confidence interval. The lecture provided examples comparing wine ratings and working memory training scores to demonstrate applying the t-tests.
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The document discusses the general linear model (GLM) and dummy coding. It can be summarized as follows:
1) The GLM is a mathematical framework used for statistical analyses like multiple regression and ANOVA. ANOVA is a type of multiple regression.
2) The GLM assumes linear relationships between variables and additive effects of predictor variables. However, it can accommodate non-linear and non-additive effects through transformations or moderation analysis.
3) Dummy coding is used to represent categorical predictor variables in a regression. It involves creating binary variables that code the categories, allowing them to be included as predictors.
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1) The lecture is divided into three segments covering multiple regression, matrix algebra, and estimation of coefficients.
2) Multiple regression allows predicting an outcome variable from multiple predictor variables using a regression equation. Matrix algebra concepts like correlation matrices are used.
3) Estimation of coefficients involves minimizing the sum of squared residuals to find the coefficient values (B) that provide the best fitting regression model. The normal equations are used to calculate the coefficient estimates.
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The document summarizes a lecture on null hypothesis significance testing (NHST). It outlines the overview of NHST, including defining the null and alternative hypotheses, types of tests, and the four possible outcomes from rejecting or retaining the null hypothesis. It then discusses problems with NHST, such as it being biased by sample size, having an arbitrary decision rule, encouraging weak hypothesis testing, being error-prone, and using questionable logic. Remedies suggested include supplementing NHST with effect sizes, avoiding arbitrary significance thresholds, learning other testing methods, replicating studies, and reporting confidence intervals.
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Lecture slides stats1.13.l06.air
This document summarizes key concepts from a statistics lecture on measurement, including reliability, validity, and sampling. The three main topics covered are:
1. Reliability, which estimates how close a measured value is to the true value using methods like test-retest, parallel tests, and inter-item estimates.
2. Validity, which assesses how well a test measures a construct through content, convergent, divergent, and nomological validity.
3. Sampling, the concepts of random and representative sampling, sampling error which decreases with larger sample sizes, and how standard error estimates sampling error based on sample size and variance.
Lecture slides stats1.13.l05.air
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This document summarizes key concepts from a statistics lecture, including measures of central tendency and variability. It discusses how the mean, median, and mode describe the central or typical values in a dataset. The mean is the best measure of central tendency for normally distributed data, while the median is preferred for datasets with outliers. Variability is measured by standard deviation and variance, which quantify how spread out numbers are from the mean. Examples using wine rating data from different countries illustrate these statistical concepts.
Lecture slides stats1.13.l23.air
The document provides an overview of the generalized linear model (GLM*). It discusses that GLM* extends the general linear model (GLM) to allow for non-normal distributions in outcome variables and non-linear relationships between predictors and outcomes. GLM* is appropriate when simple transformations or product terms in GLM are not sufficient. GLM* generalizes the linear model by adding a link function. Examples of GLM* models include binary logistic regression using the logit link and Poisson regression using the log link.
Lecture slides stats1.13.l03.air
This document summarizes key points from Lecture 3 of a Statistics course. It is divided into three segments. The first segment defines different types of variables: nominal, ordinal, interval, and ratio. The second segment discusses histograms and how they can reveal properties of distributions that summary statistics alone may miss. Both normal and non-normal distributions are illustrated using examples. The third segment introduces the z-scale as a standard scale in statistics, and how raw scores can be converted to z-scores and percentile ranks.
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বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Lecture slides stats1.13.l22.air
- 1. Statistics One Lecture 22 Non-parametric statistics
- 2. Two segments • Parametric vs. non-parametric statistics • Examples 2
- 3. Lecture 22 ~ Segment 1 Parametric vs. non-parametric statistics
- 4. Parametric vs. non-parametric • Parametric statistics are used to make inferences about population parameters 4
- 5. Parametric vs. non-parametric • Examples covered in this course • • • • • Regression coefficient Regression model R-squared Group mean Difference between group means Proportion of cases distributed across categories 5
- 6. Parametric vs. non-parametric • In all these cases inferences were based on the sample statistics as well as NHST and/or confidence intervals 6
- 7. Parametric vs. non-parametric • As well, in all these cases we assumed a particular sampling distribution • t • F • Chi-square 7
- 11. Parametric vs. non-parametric • Inferences about population parameters are not valid if all assumptions are not met • When assumptions are violated sometimes a “quick fix” is possible and the parametric approach is still considered to be valid 11
- 12. Parametric vs. non-parametric • However, sometimes it is best to abandon the parametric approach and use a nonparametric procedure 12
- 13. Parametric vs. non-parametric • Non-parametric statistics do not assume that the data or population have any characteristic structure 13
- 15. Parametric vs. non-parametric • Correlation • Spearman’s rank correlation coefficient • Kendall’s tau • Regression • Non-parametric regression 15
- 16. Parametric vs. non-parametric • t-tests • Independent: Mann-Whitney U test • Dependent: Wilcoxan signed-rank test • ANOVA • Kruskal-Wallis one-way • Friedman two-way • Chi-square • McNemar’s test • Fisher’s exact test 16
- 17. Segment summary • Parametric statistics are used to make inferences about population parameters 17
- 18. Segment summary • Inferences about population parameters are not valid if all assumptions are not met • When assumptions are violated sometimes a “quick fix” is possible and the parametric approach is still considered to be valid 18
- 19. Segment summary • However, sometimes it is best to abandon the parametric approach and use a nonparametric procedure 19
- 20. Segment summary • Non-parametric statistics do not assume that the data or population have any characteristic structure • Many procedures available 20
- 21. END SEGMENT
- 22. Lecture 22 ~ Segment 2 Non-parametric statistics Examples
- 23. Non-parametric examples • Two simple and popular tests covered in this course • Paired samples t-test • Independent t-test 23
- 24. Non-parametric examples • Here we will illustrate the non-parametric equivalent of these tests • Wilcoxan’s ranking method • Mann-Whitney U test 24
- 25. Wine tasting! 25
- 26. Non-parametric examples • Wilcoxan ranking method (Wilcoxan, 1945) – Null hypothesis • Median difference between pairs = 0 – Alternative hypothesis • Median difference between pairs != 0 26
- 28. Wilcoxan: Null hypothesis true Red White Sign(S) ABS Rank(R) S*R 1 65 60 -‐1 5 1 -‐1 2 60 70 +1 10 2 2 3 65 80 +1 15 3 3 4 85 65 -‐1 20 4 -‐4 28
- 29. Wilcoxan: Null hypothesis false Red White Sign(S) ABS Rank(R) S*R 1 65 70 +1 5 1 1 2 60 70 +1 10 2 2 3 65 80 +1 15 3 3 4 65 85 +1 20 4 4 29
- 31. Non-parametric examples • Mann Whitney U (Mann & Whitney, 1947) – Adapted Wilcoxan’s ranking method to compare independent groups – Arrange all observations, regardless of group, into a single ranked series 31
- 32. Non-parametric examples • Mann Whitney U (Mann & Whitney, 1947) – The sum of all ranks, Sum = N(N+1) / 2 • For example, Sum = 4(5) / 2 = 10 – R = Sum of Ranks from one group – U = Sum - R 32
- 33. Mann Whitney: Null hypothesis true Ra3ng Group Rank(R) 1 60 A 1 2 70 B 2 3 80 B 3 4 90 A 4 33
- 34. Mann Whitney: Null hypothesis false Ra3ng Group Rank(R) 1 60 A 1 2 70 A 2 3 80 B 3 4 90 B 4 34
- 37. Segment summary • Two simple and popular tests covered in this course • Paired samples t-test • Independent t-test 37
- 38. Segment summary • Here we illustrated the non-parametric equivalent of these tests • Wilcoxan’s ranking method • Mann-Whitney U test 38
- 39. END SEGMENT
- 40. END LECTURE 22