How do you compare histograms with different sample sizes?
If you really need to compare histograms at different sample sizes, scale them both to area 1 (i.e. to be density estimates).
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 ).
How can I compare groups with unequal sample sizes?
One group is n=4 and the other is n=68. The n=4 group doesn’t have enough subjects to really test for normality so I’m not sure if a t-test for independent means will work. I’m thinking probably a Mann-Whitney U test. Any suggestions? Is it even possible to compare the means between the two groups with such a difference in size?
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%.
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.
Do you need to undersample to make sample sizes equal?
So this undersampling to make the sample sizes equal seems to corroborate the idea that the number of days between targeting and engagement for these two groups is, in fact, different. The answer to your bolded question is no. (And you don’t need to upsample or downsample anything.)