Hi, everyone，there are many methods to see whether two sets of data are different. For example, if we have three replicates of one gene between A sample and B sample, we could make a t-test to get a p-value, if p-value is below 0.01, we say these two groups are significantly different. However, if p-value is above 0.05, to what extent can we say the two groups are similar?
For example(in R code):
we get a p-value to be 0.082, can we say x and y are similar? Another example(in R code):
This time, we get a p-value of 0.943, can we say x and y are similar?
Specifically, when we evaluate the gene effect on rice yield, we have yield data of three over-expression line and three wild type(same genome), we make a t-test or some other method, when the p-value is above 0.05, can we say this gene does not affect the grain yield? Or in arabidopsis, if we get a large p-value of tiller number between mutant and wild type, can we say this gene have no effect on tiller number phenotype? Taken first example into consideration, we get a large p-value, but it seems hard to say that x and y are similar, we just need more replicates to decide it. But in the second example, we have confidence to say that x and y are similar, right?
So can we set a threshold of 0.7, if p-value is larger than 0.7, we say two samples are similar? Or is there other methods to evaluate the similarity of two sample? How similar two sample are?
Thank you very much for your attention!