Put simply: if you test a random event often enough, you're going to get lucky eventually. In statistics, the question everybody asks is whether or not a given

Put simply: if you test a random event often enough, you're going to get lucky eventually. In statistics, the question everybody asks is whether or not a given result is "significant". To answer this question, one calculates a p-value, which gives you the probability that your data are purely random (i.e. not due to any causal factor). By convention, if p < 0.05, one calls this result "significant". There are various tests to calculate this p-value, depending on the type of data you have available. One of the most common types are t-tests, which are used when you wish to compare two samples. (For example, you might want to compare the number of butterfly species on mountain meadows with the number of species in vineyards.) The t-test takes various factors into account, including the mean and the variance of your datasets. This makes it a pretty good tool for comparison, much better than looking merely at, say, the arithmetic mean. The problem is that occasionally, the t-test is going to give you a false positive. That means you end up thinking that two things are causally linked that in reality are completely independent. The probability of such a wrong result rises with the number of tests you perform in a given study. That is why such studies need to adjust for multiple testing. Often this is done with the Bonferroni method, which basically means multiplying your p-value by a/n, where a is your level of significance (0.05) and n is the number of tests you perform. Hope that made sense :-)