When should I use the ANOVA 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. A. Compare two treatment groups when a normal distribution cannot be assumed. B. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. C. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. D. 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. E. Test to compare frequency data to a specific population model F. Compare two treatments consisting of paired data where a normal distribution can be assumed. G. Test to see if the frequency data from a population fit a discrete probability distribution. H. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. I. More than two treatment groups where a normal distribution can be assumed. J. Compare more than two treatment groups when a normal distribution cannot be met. K. Test the fit of the normal distribution to the data set. L. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. M. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution.
When should I use the ANOVA 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. A. Compare two treatment groups when a normal distribution cannot be assumed. B. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. C. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. D. 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. E. Test to compare frequency data to a specific population model F. Compare two treatments consisting of paired data where a normal distribution can be assumed. G. Test to see if the frequency data from a population fit a discrete probability distribution. H. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. I. More than two treatment groups where a normal distribution can be assumed. J. Compare more than two treatment groups when a normal distribution cannot be met. K. Test the fit of the normal distribution to the data set. L. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. M. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution.
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
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Transcribed Image Text:When should I use the ANOVA 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.
A. Compare two treatment groups when a normal distribution cannot be assumed.
B. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance.
C. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution.
D. 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.
E. Test to compare frequency data to a specific population model
F. Compare two treatments consisting of paired data where a normal distribution can be assumed.
G. Test to see if the frequency data from a population fit a discrete probability distribution.
H. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean.
I. More than two treatment groups where a normal distribution can be assumed.
J. Compare more than two treatment groups when a normal distribution cannot be met.
K. Test the fit of the normal distribution to the data set.
L. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis.
M. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution.
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