1 Why reported effect sizes are inflated 2 Setup 3 Exploration 1 4 Unconditional means, power, and sign error 5 Conditional means 5.1 filter = 0.05 5.2 filter = 0.2 1 Why reported effect sizes are inflated This post is motivated by many discussions in Gelman’s blog but start here When we estimate an effect1, the estimate will be a little inflated or a little diminished relative to the true effect but the expectation of the effect is the true effect.

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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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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