Why are different statistical Analyses used with different sample sizes?

Why are different statistical Analyses used with different sample sizes?

Unequally sized groups are common in research and may be the result of simple randomization, planned differences in group size or study dropouts. Unequal sample sizes can lead to: Unequal variances between samples, which affects the assumption of equal variances in tests like ANOVA.

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 does unequal sample size affect t test?

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.

How do you compare two groups with different variances?

How to compare two means when the groups have different standard deviations.

1. Conclude that the populations are different.
2. Transform your data.
3. Ignore the result.
4. Go back and rerun the t test, checking the option to do the Welch t test that allows for unequal variance.
5. Use a permuation test.

What is the difference between a one-way ANOVA and a two-way Anova?

A one-way ANOVA only involves one factor or independent variable, whereas there are two independent variables in a two-way ANOVA. In a one-way ANOVA, the one factor or independent variable analyzed has three or more categorical groups. A two-way ANOVA instead compares multiple groups of two factors.

Can ANOVA be used for 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 ).

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

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 can you compare two groups with different 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.

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.

How are sample sizes related to the power of an experiment?

Table 1: Comparison of the power of an experiment using complete randomisation (equal sample sizes) and the average power of an experiment using simple randomisation (possibly unequal sample sizes). The R code for the comparison is at the bottom of this post.

What is the statistical advantage of equal sample sizes?

Additionally, while equal-sized groups maximise statistical power, the advantage is easily overstated. An experiment with 30+30 participants has a 76% chance to detect a systematic difference of 0.7 standard deviations between the two group means; for an experiment with 20+40 participants, this probability is 71%.