My question is, if I only sampled 4 individuals, but then ran my stats tests against very large numbers of something contained within those 4 individuals, is this valid? To take it to its extreme, I could have compared 1 individual against 1 other (n=1) and still have done stats tests on cell numbers which would seem very high powered, but it's only powered for those two individuals. So the p value with these large numbers of cells comes out tiny. So for example, if I had a mean of 10,000 type A cells in group 1 and 1,000 type A cells in group 2, I also had 990,000 other cells in group 1 and 999,000 other cell types in group 2. I used the Chi squared test for a difference between two different non-parametric samples. For specific cell types in that mix, I created a contingency table and tested for a difference between mean cell proportions in the sample. ![]() For simplicity let's say n=1 million (mixed cells). The two columns represent two alternative outcomes. ![]() ![]() The rows represent two different treatments. Most contingency tables have two rows (two groups) and two columns (two possible outcomes), but Prism lets you enter tables with any number of rows and columns. The samples each contained millions of cells and I wanted to compare the cell populations for a difference. Analyses performed from a contingency table Chi-square and Fisher’s exact test (also computes odds ratios and relative risk) Fraction of total. I recently did flow cytometry to quantify cell numbers on 4 similar samples from one group and 4 from another different group (n=4).
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