This site defines power and explains what factors may affect it, such as significance level, sample size and variance.
This site defines power and explains what factors may affect it, such as significance level, sample size and variance.
A song to teach about the relationship between the slope of the regression line and the correlation. The lyric was authored by Lawrence Mark Lesser from the University of Texas at El Paso. The song may be sung to the tune of the English lullaby "Twinkle Twinkle Little Star" (Jane Taylor, 1806). Free for use in non-commercial teaching. This song is also 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).
Song is simply a quick jingle to help students recall the conceptual interpretation of a p-value. May be sung to tune of "Row, Row, Row Your Boat". Recorded June 26, 2009 at the OSU Whisper Room: Larry Lesser, vocals/guitar; Justin Slauson, engineer. 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).
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).
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).
This group activity focuses on conducting an experiment to determine which of two brands of paper towels are more absorbent by measuring the amount of water absorbed. A two-sample t-test can be used to analyze the data, or simple graphics and descriptive statistics can be used as an exploratory analysis. Students are asked to think about design issues, and to write a short report stating their results and conclusions, along with an evaluation of the experimental design. Key words: Two-sample t-test
This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test. They carry out further simulations to estimate the power of the test. After pooling their data with that of other groups, they construct a power curve. A theoretical power curve is also constructed, and the students discuss why there are differences between the expected and estimated curves. Key words: Power, size, hypothesis testing, binomial distribution
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.
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.