In what situation should chi-square independence test be applied?
The Chi-square test of independence checks whether two variables are likely to be related or not. We have counts for two categorical or nominal variables. We also have an idea that the two variables are not related. The test gives us a way to decide if our idea is plausible or not.
When can chi-square test not be used?
Most recommend that chi-square not be used if the sample size is less than 50, or in this example, 50 F2 tomato plants. If you have a 2×2 table with fewer than 50 cases many recommend using Fisher’s exact test.
What is the difference between a chi-square test of homogeneity and independence?
The difference is a matter of design. In the test of independence, observational units are collected at random from a population and two categorical variables are observed for each unit. In the test of homogeneity, the data are collected by randomly sampling from each sub-group separately.
Can chi-square test be used for more than two categories?
The critical value in the table is , and we conclude that there are no significant differences between males and females in their political affiliations. Chi-square can also be used with more than two categories.
Why would you use a chi-square test?
You use a Chi-square test for hypothesis tests about whether your data is as expected. The basic idea behind the test is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. Both tests involve variables that divide your data into categories.
Why would you use a chi-square?
The Chi-Square test is a statistical procedure used by researchers to examine the differences between categorical variables in the same population. For example, imagine that a research group is interested in whether or not education level and marital status are related for all people in the U.S.
What does P less than .05 mean?
significant difference
What does P value 0.005 mean?
0.5%
What does p value 0.01 mean?
The p-value is a measure of how much evidence we have against the null hypothesis. A p-value less than 0.01 will under normal circumstances mean that there is substantial evidence against the null hypothesis.