Researchers frequently report results as relative effects, for example, “Male flies from selected lines had 50% larger upwind flight ability than male flies from control lines (Control mean: 117.5 cm/s; Selected mean 176.5 cm/s).” where a relative effect is \[\begin{equation} 100 \frac{\bar{y}_B - \bar{y}_A}{\bar{y}_A} \end{equation}\] If we are to follow best practices, we should present this effect with a measure of uncertainty, such as a confidence interval. The absolute effect is 59.

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On page 606, of Lock et al “Statistics: Unlocking the Power of Data”, the authors state in item D “The p-value from the ANOVA table is 0.000 so the model as a whole is effective at predicting grade point average.” Ah no. library(data.table) library(mvtnorm) rho <- 0.5 n <- 10^5 Sigma <- diag(2) Sigma[1,2] <- Sigma[2,1] <- rho X <- rmvnorm(n, mean=c(0,0), sigma=Sigma) colnames(X) <- c("X1", "X2") beta <- c(0.01, -0.

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On alpha

This post is motivated by Terry McGlynn’s thought provoking How do we move beyond an arbitrary statistical threshold? I have been struggling with the ideas explored in Terry’s post ever since starting my PhD 30 years ago, and its only been in the last couple of years that my own thoughts have begun to gel. This long marination period is largely because of my very classical biostatistical training. My PhD is from the Department of Anatomical Sciences at Stony Brook but the content was geometric morphometrics and James Rohlf was my mentor for morphometrics specifically, and multivariate statistics more generally.

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Here is the motivating quote for this post, from Andrew Gelman’s blog post “Five ways to fix statistics” I agree with just about everything in Leek’s article except for this statement: “It’s also impractical to say that statistical metrics such as P values should not be used to make decisions. Sometimes a decision (editorial or funding, say) must be made, and clear guidelines are useful.” Yes, decisions need to be made, but to suggest that p-values be used to make editorial or funding decisions—that’s just horrible.

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R doodles. Some ecology. Some physiology. Much fake data.

Thoughts on R, statistical best practices, and teaching applied statistics to Biology majors

Jeff Walker, Professor of Biological Sciences

University of Southern Maine, Portland, Maine, United States