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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Paired line plots

load libraries make some fake data make a plot with ggplot ggplot scripts to draw figures like those in the Dynamic Ecology post Paired line plots (a.k.a. “reaction norms”) to visualize Likert data load libraries library(ggplot2) library(ggpubr) library(data.table) make some fake data set.seed(3) n <- 40 self <- rbinom(n, 5, 0.25) + 1 others <- self + rbinom(n, 3, 0.5) fd <- data.table(id=factor(rep(1:n, 2)), who=factor(rep(c("self", "others"), each=n)), stigma <- c(self, others)) make a plot with ggplot The students are identified by the column “id”.

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set up The goal is to plot the measure of something, say O2 levels, against depth (soil or lake), with the measures taken on multiple days library(ggplot2) library(data.table) First – create fake data depths <- c(0, seq(10,100, by=10)) dates <- c("Jan-18", "Mar-18", "May-18", "Jul-18") x <- expand.grid(date=dates, depth=depths) n <- nrow(x) head(x) ## date depth ## 1 Jan-18 0 ## 2 Mar-18 0 ## 3 May-18 0 ## 4 Jul-18 0 ## 5 Jan-18 10 ## 6 Mar-18 10 X <- model.

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Author's picture

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