Significance Testing Principles

  • Measures of the size of an effect based on the degree of overlap between groups usually involve calculating the proportion of the variance that can be explained by differences between groups. This resource outlines different approaches to measuring this proportion.

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  • A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).

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  • A song for use in helping students to identify counterparts in the courtroom analogy for hypothesis tests (innocence ≈ null; acquit ≈ fail to reject; etc…) and to identify errors of Type I and II in context.  Lyrics by Larry Lesser and music by Larry Lesser and Dominic Sousa in 2015, both from The University of Texas at El Paso.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to reason about how larger sample sizes decrease the p-value, all else being equal.  Lyrics by Larry Lesser and music by Dominic Sousa in 2015, both from The University of Texas at El Paso.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • This is the free online textbook for the Foundations of Data Science class at UC Berkeley for the Data 8 Project. Creators have used https://github.com/data-8/textbook to maintain this textbook (an open source project that allows for continual easy editing and maintenance).

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  • 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.

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  • 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.

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  • This handout lists the most commonly used effect sizes, adjustments, and rules of thumb concerning sample size calculation. 

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  • 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.

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  • Check how your Bayes factor conclusion depends on the r-scale parameter.

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