How do you analyze Anova results?
Interpret the key results for One-Way ANOVAStep 1: Determine whether the differences between group means are statistically significant.Step 2: Examine the group means.Step 3: Compare the group means.Step 4: Determine how well the model fits your data.Step 5: Determine whether your model meets the assumptions of the analysis.
What are the assumptions for a two sample t test?
Two-Sample T-Test AssumptionsThe assumptions of the two-sample t-test are: The data are continuous (not discrete).The data follow the normal probability distribution. The two samples are independent. Both samples are simple random samples from their respective populations.
What is an example of a parametric test?
Parametric tests assume a normal distribution of values, or a “bell-shaped curve.” For example, height is roughly a normal distribution in that if you were to graph height from a group of people, one would see a typical bell-shaped curve.
What are the criteria for a parametric test?
Reasons to Use Parametric TestsParametric analysesSample size guidelines for nonnormal data1-sample t testGreater than 202-sample t testEach group should be greater than 15One-Way ANOVAIf you have 2-9 groups, each group should be greater than 15. If you have 10-12 groups, each group should be greater than 20.
How do you determine if a test is parametric or nonparametric?
If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size.