# Can you do an ANOVA with unequal sample sizes?

## Can you do 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 do a t-test with unequal sample sizes?**

Even though you can perform a t-test when the sample size is unequal between two groups, it is more efficient to have an equal sample size in two groups to increase the power of the t-test. Welch’s t-test is for unequal variance data.

### Can you do ANOVA with unequal variance?

Let me acquaint you with Welch’s ANOVA. You use it for the same reasons as the classic statistical test, to assess the means of three or more groups. However, Welch’s analysis of variance provides critical benefits and protections because you can use it even when your groups have unequal variances.

**Why are unequal sample sizes bad?**

The statistical results are only approximate. Unequal sample sizes result in confounding. Unequal sample sizes indicate a poor experimental design.

#### Can you compare data with different sample sizes?

The difference in the sample sizes cannot invalidate the method. The assumption of a normal distribution can make a big difference here, and having 500 values is a good amount of data to see if this assumption would be too unreasonable.

**How do you know if variances are equal or unequal?**

An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the variances are not equal.

## What is Welch’s correction?

Welch’s Test for Unequal Variances (also called Welch’s t-test, Welch’s adjusted T or unequal variances t-test) is a modification of Student’s t-test to see if two sample means are significantly different.

**What is unbalanced ANOVA?**

The term “unbalanced” means that the sample sizes nkj are not all equal. A balanced design is one in which all nkj = n. In the unbalanced case, there are 2 ways to define sums of squares for factors A and B.

### What is an unbalanced ANOVA?

The term “unbalanced” means that the sample sizes nkj are not all equal. A balanced design is one in which all nkj = n. In the unbalanced case, there are 2 ways to define sums of squares for factors A and B. 1.

**How do you know if two samples are statistically different?**

Using the 1-Sample Sign Test for Paired Data The paired t-test is used to check whether the average differences between two samples are significant or due only to random chance. In contrast with the “normal” t-test, the samples from the two groups are paired, which means that there is a dependency between them.

#### What statistics is used to compare groups with different sample sizes?

Analysis of Variance (ANOVA) for Comparing Multiple Means In order to compare the means of more than two samples coming from different treatment groups that are normally distributed with a common variance, an analysis of variance is often used.

**What happens if the sample sizes in a Nested ANOVA are unequal?**

When the sample sizes in a nested anova are unequal, the P values corresponding to the F-statistics may not be very good estimates of the actual probability. For this reason, you should try to design your experiments with a “balanced” design, meaning equal sample sizes in each subgroup.

## What are the limitations of one-way ANOVA?

The main practical issue in one-way ANOVA is that unequal sample sizes affect the robustness of the equal variance assumption. ANOVA is considered robust to moderate departures from this assumption. But that’s not true when the sample sizes are very different.

**How does sample size affect the robustness of ANOVA?**

Assumption Robustness with Unequal Samples The main practical issue in one-way ANOVA is that unequal sample sizes affect the robustness of the equal variance assumption. ANOVA is considered robust to moderate departures from this assumption. But that’s not true when the sample sizes are very different.

### What is the minimum sample size required to run ANOVA?

There is no equal sample size assumption for ANOVA. If your data satisfies the 3 assumptions (Normality, equality of variance and independence) you can run ANOVA.