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When should I use the Mann-Whitney U-test? Remember that some tests, such as chi squared, can be used under various circumstances. The goal of the test changes based on the situation. Pay attention to the specific conditions noted in parenthesis to ensure you are picking the correct goal. O More than two treatment groups where a normal distribution can be assumed. O Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. O Compare more than two treatment groups when a normal distribution cannot be met. O Compare two treatment groups when a normal distribution cannot be assumed. O Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. O Compare two treatments consisting of paired data where a normal distribution can be assumed. Test the fit of the normal distribution to the data set. O Test if the median of a data set equals a null hypothesized value when the distribution of the data does not meet the assumption of normalacy. O Compare two treatment groups consisting of paired data when the data do not fit the normal distribution. O Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. O Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. O Test to see if the frequency data from a population fit a discrete probability distribution. O Test to compare frequency data to a specific population model

When should I use the Mann-Whitney U-test? Remember that some tests, such as chi squared, can be used under various circumstances. The goal of the test changes based on the situation. Pay attention to the specific conditions noted in parenthesis to ensure you are picking the correct goal. O More than two treatment groups where a normal distribution can be assumed. O Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. O Compare more than two treatment groups when a normal distribution cannot be met. O Compare two treatment groups when a normal distribution cannot be assumed. O Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. O Compare two treatments consisting of paired data where a normal distribution can be assumed. Test the fit of the normal distribution to the data set. O Test if the median of a data set equals a null hypothesized value when the distribution of the data does not meet the assumption of normalacy. O Compare two treatment groups consisting of paired data when the data do not fit the normal distribution. O Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. O Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. O Test to see if the frequency data from a population fit a discrete probability distribution. O Test to compare frequency data to a specific population model

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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When should l use the Mann-Whitney U-test? Remember that some tests, such as chi squared, can be used under various circumstances. The goal of the test changes based on the situation. Pay attention to the specific conditions noted in parenthesis to ensure you are picking the correct goal. More than two treatment groups where a normal distribution can be assumed. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. Compare more than two treatment groups when a normal distribution cannot be met. Compare two treatment groups when a normal distribution cannot be assumed. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. Compare two treatments consisting of paired data where a normal distribution can be assumed. Test the fit of the normal distribution to the data set. Test if the median of a data set equals a null hypothesized value when the distribution of the data does not meet the assumption of normalacy. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. Test to see if the frequency data from a population fit a discrete probability distribution. O Test to compare frequency data to a specific population model
Transcribed Image Text:When should l use the Mann-Whitney U-test? Remember that some tests, such as chi squared, can be used under various circumstances. The goal of the test changes based on the situation. Pay attention to the specific conditions noted in parenthesis to ensure you are picking the correct goal. More than two treatment groups where a normal distribution can be assumed. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. Compare more than two treatment groups when a normal distribution cannot be met. Compare two treatment groups when a normal distribution cannot be assumed. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. Compare two treatments consisting of paired data where a normal distribution can be assumed. Test the fit of the normal distribution to the data set. Test if the median of a data set equals a null hypothesized value when the distribution of the data does not meet the assumption of normalacy. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. Test to see if the frequency data from a population fit a discrete probability distribution. O Test to compare frequency data to a specific population model
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