What Each Test Does:
T-Tests: These tests compare averages (means) between two groups. But, it can be tricky to know which type of T-Test to use—independent or paired.
Chi-Square Tests: These tests look at relationships between different categories. The tricky part is making sure the expected numbers in each category are large enough. If not, the results might be misleading.
ANOVA: This test compares averages among three or more groups. However, it can get complicated, especially if there are interactions between the groups.
Data Needs:
T-Tests: These tests usually require the data to follow a normal distribution, which doesn’t always happen. Sometimes, we may need to use other methods or change the data, making things more complicated.
Chi-Square Tests: These tests need a good number of samples (data points). If there aren’t enough, the results may not be reliable.
ANOVA: This test assumes that the variances (the differences among groups) are similar. We can check this with Levene’s test, but it’s easy to misunderstand the results.
To handle these challenges, it's important to do a careful look at the data first. Using good statistical software can help check if the assumptions are right and help pick the best test to use.
What Each Test Does:
T-Tests: These tests compare averages (means) between two groups. But, it can be tricky to know which type of T-Test to use—independent or paired.
Chi-Square Tests: These tests look at relationships between different categories. The tricky part is making sure the expected numbers in each category are large enough. If not, the results might be misleading.
ANOVA: This test compares averages among three or more groups. However, it can get complicated, especially if there are interactions between the groups.
Data Needs:
T-Tests: These tests usually require the data to follow a normal distribution, which doesn’t always happen. Sometimes, we may need to use other methods or change the data, making things more complicated.
Chi-Square Tests: These tests need a good number of samples (data points). If there aren’t enough, the results may not be reliable.
ANOVA: This test assumes that the variances (the differences among groups) are similar. We can check this with Levene’s test, but it’s easy to misunderstand the results.
To handle these challenges, it's important to do a careful look at the data first. Using good statistical software can help check if the assumptions are right and help pick the best test to use.