G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, ztests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.
G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, ztests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.
This site offers separate webpages about statistical topics relevant to those studying psychology such as research design, representing data with graphs, hypothesis testing, and many more elementary statistics concepts. Homework problems are provided for each section.
This handout lists the most commonly used effect sizes, adjustments, and rules of thumb concerning sample size calculation.
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
Check how your Bayes factor conclusion depends on the r-scale parameter.
This Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.
When does a significant p-value indicate a true effect? This app will help with understanding the Positive Predictive Value (PPV) of a p-value.
This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124
Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?
Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.
Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app.