# Statistically significant equality of sample sizes (does 50 equal to 53)***?

Significance tests are used for a variety of reasons and in many research scenarios. One of them is checking the equality of sample sizes.

There's a book for psychologists whose authors recommend chi-squared test to check if two samples are of the same size. First sample size is 50 and the other one is 53. Then they use chi-squared test to find out if 50 is statistically equal to 53. If it is, then a researcher can state equal sample sizes (f.e. for t-tests or ANOVA, etc).

Q: Isn't it a really bad way to use significance tests?

• @kjetilbhalvorsen I'm taking your question as a recognition for my English ;) The book is all in Polish. However, do you think that's bad advise? Commented Nov 10, 2020 at 12:58
• Is this test really usefull ? You can do T-tests or ANOVA with different sample sizes, it's a bit more complex, but using R, it's alright. It does seem to be a poor use of testing. Commented Nov 10, 2020 at 13:03
• Still, can you reference the book (and page number)? I speak Polish and I'm really curoius to see how authors justify their recommendations... Commented Nov 10, 2020 at 14:12
• "Drogowskaz statystyczny" (2007), p.183. Commented Nov 10, 2020 at 14:38
• It makes no sense whatsoever. It makes sense to test for the prevalence of groups in the population, not to test in-sample sizes. In-sample, if one group is sized 50 and the other 53, then you already know, with 100% certainty, that both samples are of different sizes. Commented Nov 30, 2020 at 12:50