Let us suppose, we have a time series data of a variable and I am interested in change in the distribution of that variable over two time frames of equal size (long enough to discard pathologies related to sample size). It is found that at the first time window, the distribution is bimodal and in the second, it translates to a unimodal system. My querry is, in general, how can we explain these change from complex adaptive system perspective?
A few questions.
1) What sample size is large enough to allow us to assume that pathologies related to sample size are not an issue?
2) Might there be a seasonal effect?
3) Might this be an effect of years with the first time frame in 2016 and the second one of equal length in 2017?
3) This could be a "treatment" effect. The treatment does not have to be applied by the researcher, but could be the same pond before and after a pollution event.
4) That you can see a unimodal to bimodal shift means that you are measuring something. Gene expression, body weight, prevalence of a disease, abundance, biodiversity, or something else?
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