I am a HS Bio teacher and doing a microevolution lab involving candy. Essentially students use four candy types and push them together until one cracks (Nat Sel). They also do simulations of migration, mutation and genetic drift. They then calculate the "allele frequency" of each type per generation and look for changes. I want to have my students do a simple statistical test to see whether the change was significant or not from each evolutionary factor. I was going to do a chi square test, but someone told me that is not appropriate to this type of experiment. So, can anyone confirm, is chi square inappropriate, and if it is.. Can anyone point me to a more appropriate test. These are high school kids with no stats background (and I have forgotten virtually all the stats I learned 20 years ago)...

  • $\begingroup$ It seems that you're looking for one test that would fit all situations encountered in evolutionary biology. This obviously does not exist. You should provide more information about what you want to test and describe the kind of data you have. And then, I am afraid the question might be off-topic as it is about statistics and not about biology. You may want to try to ask your question on crossvalidated.SE $\endgroup$
    – Remi.b
    Apr 8, 2014 at 14:53
  • $\begingroup$ You may have a point on the off topic bit, though I've already started, so.... :) To clarify the lab a bit: Students press candies together over three rounds of "natural selection" (starting with 12 candies of each type- m&ms, skittles, etc- 4 types total). Survivors go on to the next round and replicate when the population gets below a certain number. We do another round where kids pour the candy on desks pushed together and some bounce in to the neighbors areas to represent migration, and another simulation for a bottleneck. Students then look at how much each factor affected the #s. $\endgroup$ Apr 9, 2014 at 13:58

1 Answer 1


To choose the right statistical method (it is more than just saying "use the t-test") you need to think about your experiment. A good starting point is this figure from Bitesizebio:

enter image description here

There are two relevant articles on that website:

Probably also interesting is the definition of statistical terms:


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