What happens when there is an unequal sample size?

What happens when there is an unequal sample size?

The section on Multi-Factor ANOVA stated that when there are unequal sample sizes, the sum of squares total is not equal to the sum of the sums of squares for all the other sources of variation. This is because the confounded sums of squares are not apportioned to any source of variation.

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.

How are null hypothesis and sample size calculated?

Both have two categorical variables. Both count the the frequencies of the combinations of these categories. They calculate the test statistic the same way. Without getting into the math, it’s basically a comparison of the actual frequencies of the combinations with the frequencies you’d expect under the null hypothesis.

How to calculate the correlation coefficient of two variables?

If you have two variables with different sizes, they are not paired, and it is not possible to calculate the correlation coefficient of both variables. DESIGN: Assume that the data is quantitative, you might need to re-design the two data sets into matching pairs and then calculate the correlation coefficient in a group of 10.

Can a correlation be used at the same time?

And in principle, the data should be sampled at the same time for it to be meaningful using conventional correlation measures. As a more general problem, I would add that there are techniques to deal with irregularly spaced time series data.

Is there a correlation between Y and X?

No amount of imputation, time series analysis, GARCH models, interpolation, extrapolation, or other fancy algorithms will do anything to create information where it does not exist (although they can create that illusion ;-). The history of Y’s price before X went public is useless for assessing their subsequent correlation.

Why do you need an equal sample size for a t test?

The reason sample size matters is that unequal variances don’t pose a problem for a t-test with equal sample sizes. So as long as your sample sizes are equal, you don’t have to worry about homogeneity of variances.

Do you make assumptions about the sample size?

Statistical tests do not make assumptions about sample size. There are, of course, differing assumptions with various tests (e.g., normality), but the equality of sample sizes is not one of them.

When do unequal sample sizes occur in factorial ANOVA?

Factorial ANOVA includes all those ANOVA models with more than one crossed factor. It generally involves one or more interaction terms. Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this.

How can I deal with uneven sample sizes in my study?

So it should be feasible to compare the two groups. But if there’s an entire group, consisting of men and women; and you’ve got a 5% response rate from males and 50% from females, you can use the method of weighting.

What’s the difference between 500 and 4 samples?

However both of them are of highly different sizes, i.e one has 500 samples while the other has 4. I want to determine if the differences between the samples is statistically significant. I thought of using an unpaired t-test, but I am not sure if the difference in sample sizes would invalidate the method.