Can you run an ANOVA with unequal sample sizes?

You can perform one way ANOVA with unequal sample sizes. You must consider the assumptions of Normality, equality of variance and independence ( that mentioned by Saigopal ) before using ANOVA and in a case of not correct assumption then you must use non-parametric test ( Kruskal-Wallis test ).

Can I use t-test for unequal sample sizes?

If sample sizes in both conditions are equal, the t-test is very robust against unequal variances. If sample sizes are unequal, unequal variances can influence the Type 1 error rate of the t-test by either increasing or decreasing the Type 1 error rate from the nominal (often 0.05) alpha level.

Can you do a two way ANOVA with unequal sample sizes?

If you have unequal variances and equal sample sizes, no problem. The only problem is if you have unequal variances and unequal sample sizes.

How do you know if variances are equal or unequal?

There are two ways to do so:

  1. Use the Variance Rule of Thumb. As a rule of thumb, if the ratio of the larger variance to the smaller variance is less than 4 then we can assume the variances are approximately equal and use the Student’s t-test.
  2. Perform an F-test.

What is a good sample size for t-test?

The parametric test called t-test is useful for testing those samples whose size is less than 30. The reason behind this is that if the size of the sample is more than 30, then the distribution of the t-test and the normal distribution will not be distinguishable.

What is the minimum sample size for t-test?

10 Answers. There is no minimum sample size for the t test to be valid other than it be large enough to calculate the test statistic.

How do you test for unequal variances?

How the unequal variance t test is computed

  1. Calculation of the standard error of the difference between means. The t ratio is computed by dividing the difference between the two sample means by the standard error of the difference between the two means.
  2. Calculation of the df.

How to perform one way ANOVA with unequal sample sizes in R?

Suppose we have a categorical column defined as Group with four categories and a continuous variable Response both stored in a data frame called df then the one-way anova can be performed as − ‘data.frame’: 20 obs. of 2 variables: $ Group : chr “B” “B” “A” “D” $ Response: int 1 2 1 2 1 0 2 3 2 2

When to use an unpaired two sample t test?

If the unequal sample sizes are independent groups, then the mean can be parsed in R via an unpaired two-sample t-test.

How to do a two sample t test in R?

We do this in R with a Fisher’s F-test, var.test (x, y). If your p > 0.05, then you can assume that the variances of both samples are homogenous. In this case, we run a classic Student’s two-sample t-test by setting the parameter var.equal = TRUE.

When to use a one-way ANOVA in regression?

So to answer 1. explicitly, yes, you can use a one-way ANOVA when the sample sizes are extremely unequal. If the groups (A through D) were formed by categorizing BMI (a continuous variable), you would be better off using regression with BMI as your predictor; categorizing continuous variables is not a good thing to do.