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. Key concepts: Contingency tables How to: Contingency table analysis Fisher's test or chi-square test Interpreting results: Relative risk and odds ratio Interpreting results: Sensitivity and specificity Interpreting results: P values from contingency tables Analysis checklist: Contingency tables Graphing tips: Contingency tables VII. For specific cell types in that mix, I created a contingency table and tested for a difference between mean cell proportions in the sample. If you collaborate with other Prism users, you may want to keep using Prism 3 on your collaborative projects until your colleagues upgrade to Prism 4. However prior version of Prism cannot open files saved by Prism 4. PZM files created by Prism versions 2 and 3. For simplicity let's say n=1 million (mixed cells). New Prism 4 file format Prism 4 uses a new file extension. The samples each contained millions of cells and I wanted to compare the cell populations for a difference. 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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