What is the main difference between t test and chi-square test?
The t-test allows you to say either “we can reject the null hypothesis of equal means at the 0.05 level” or “we have insufficient evidence to reject the null of equal means at the 0.05 level.” A chi-square test allows you to say either “we can reject the null hypothesis of no relationship at the 0.05 level” or “we have …
When should you use the t test?
When to use a t-test A t-test can only be used when comparing the means of two groups (a.k.a. pairwise comparison). If you want to compare more than two groups, or if you want to do multiple pairwise comparisons, use an ANOVA test or a post-hoc test.
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 ANOVA and chi-square test?
The chi-square is used to investigate whether the distribution of classes and is compatible with a distribution model (often equal distribution, but not always), while ANOVA is used to investigate whether differences in means between samples are significant or not.
What is the difference between a chi square and t-test?
T-test allows you to differentiate between the two groups. While the Chi-square test also helps you to find the relationship between two variables but has no direction and size of the relationship.
What is the difference of chi-square and t test?
While the Chi-square test also helps you to find the relationship between two variables but has no direction and size of the relationship. Null hypothesis: In the T-test, there is no stat. difference between the two groups while in the Chi-square test there is no relationship between two variables. I hope this will helps you.
How do you calculate chi square test?
To calculate chi square, we take the square of the difference between the observed (o) and expected (e) values and divide it by the expected value. Depending on the number of categories of data, we may end up with two or more values. Chi square is the sum of those values.
What are the requirements for a chi square test?
Requirements for a Chi Square Test: Data is typically attribute (discrete). All data must be able to be categorized as being in some category or another. Expected cell counts should not be low (definitely not less than 1 and preferable not less than 5) as this could lead to a false positive indication…