Do sample sizes need to be equal?

Do sample sizes need to be equal?

According to Keppel (1993), there is no good rule of thumb for how unequal the sample sizes need to be for heterogeneity of variance to be a problem. So if you have equal variances in your groups and unequal sample sizes, no problem. If you have unequal variances and equal sample sizes, no problem.

How do you compare two unequal sample sizes?

One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set.

How do you find unequal variances?

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.

When are unequal sample sizes are and are not a problem?

In your statistics class, your professor made a big deal about unequal sample sizes in one-way Analysis of Variance (ANOVA) for two reasons. 1. Because she was making you calculate everything by hand. Sums of squares require a different formula* if sample sizes are unequal, but statistical software will automatically use the right formula.

Are there unequal sample sizes for mixed ANOVA?

However, all these different groups have different numbers of examinees. The first group has 490 participants, the second group has 1919 participants and the third group has 529 participants. Thus, I can say that I have unequal sample sizes for Mixed ANOVA.

When to use a two sample unpaired t-test?

The classical two-sample unpaired t-test for example assumes variance homongenity and is robust against violations only if both groups are similarily sized (in order of magnitude). Otherwise higher variance in the smaller group will lead to Type I errors.

Why are there different sample sizes in different studies?

Imbalances can arise when participants drop out of the study or when data is lost due to technical glitches. These cases are different from the previous two (simple randomisation and planned imbalances) as they needn’t be due to different numbers of participants being assigned to the experiment’s conditions.