““Let the computer find out” is a poor strategy and usually reflects the fact that the researcher did not bother to think clearly about the problem of interest and its scientific setting (Burnham and Anderson, 2002)." Note this approach has been criticized as “data-dredging” or “fishing” (Burnham and Anderson 2002, but see Symonds and Moussalli 2011) and is nicely summarized by this often-quoted line from Burnham and Anderson (2012). For example, we can compare models with all possible combinations of the predictors of interest (AKA the all-subset approach) rather than constructing models with only particular combinations of those predictors. However, detailed knowledge is often unavailable for many ecological systems and alternative approaches exist. In an ideal world, this would be based on detailed knowledge of the system you are working in and on prior work or literature reviews. The most challenging part of model selection is coming up with a series of hypothesis-driven models that that adequately capture the processes and patterns you are interested in representing. The best supported models can be averaged to get parameter estimates.Models can be ranked and weighted according to their fit to the observed data.This is what model selection allows and it is becoming increasingly used in ecology and evolutionary biology. This is an inferential or frequentist approach to statistics.Īn alternative approach is to simultaneously test multiple competing hypotheses, with each hypothesis being represented in a separate model. This low probability then allows us to reject the null hypothesis in favour of the more biologically interesting alternative hypothesis. t-statistic, F-value, etc.), and rejected the null hypothesis when the observed test statistic falls outside the test statistic distribution with some arbitrarily low probability (e.g. Up to now, when faced with a biological question, we have formulated a null hypothesis, generated a model to test the null hypothesis, summarized the model to get the value of the test-statistic (e.g. 01: Linear models and statistical modelling 19: Data wrangling in dplyr, ggplot, tidy data 10: Intro to course, programming, RStudio, and R Markdown
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