**Online Statistical Calculators, Tutorials, and Resources**

Statistical calculators include basic probability (*p*-value) calculators for the normal distribution, Student’s *t*-distribution, chi-square distribution, and the F-distribution. The normal distribution calculator is designed to be used for standard introductory statistics normal score problems. A normal score power application designed to teach the relationships among significance level (), effect size, sample size, and statistical power is forthcoming.

Additional online calculators are in development and include (1) calculating confidence intervals for standardized effect size estimates, (2) new methods for assessing mediational models, and (3) statistical power calculations based on effect size estimates. All of these I have currently written as *R* source files and I am rewriting them in Java to work as applications.

**Statistical Tutorials**

My plan over the next several months is to convert a number of my lecture notes on different topics and place them as brief tutorials on this website. These include an introduction to mediation, partial regression coefficients, examining interactions in regression, computing confidence intervals, and the importance of multilevel modeling.

**Why This Website?**

This website arises out of a number of different frustrations as both a researcher and teacher. Online teaching resources that I rely on are not reliable. Over the years different useful resources disappear. For instance, see Chip Reichardt’s repository of useful online resources that he painstakingly compiled in 1999 here. Most of the links are dead and gone. So I’ve decided to create those resources that I rely on for teaching to ensure their availability in my courses.

As a quantitative researcher I have been focused on developing solutions to common problems faced by applied researchers. I have created a number of useful tools that are available in *R*. However, as prolific as the spread of *R* has been among the quantitative community, it has not yet reached the point of being a comfortable and basic tool among the applied community broadly. That will change over time as better GUI interfaces are developed. In the interim, may I be so bold as to recommend Bill Revelle’s website on *R* in Psychology and StatMethods.net which serves as a Rosetta stone translating SPSS and SAS to *R*. Although I will likely develop and place tutorials and examples based in *R* on this website later, for the present I have been focused on developing Java applications that will allow you to use these techniques *now*. Or at least when I release them on this website in several weeks!

**About Me**

Over the past ten years I have taught a wide range of courses in statistics (e.g., introduction to statistics, advanced research methods, multiple regression, ANOVA, multilevel modeling, and structural equation modeling). My research focuses on personality, person perception, and quantitative methodology. Recent projects include determining confidence intervals for standardized effect size estimates, power analysis, and novel methods for assessing mediational models. These latter quantitative projects were originally developed and programmed in R. However, to increase the utility and availability of these computational tools I have rewritten these programs as Java applications that one can access through Java Web Start. Although I would argue that you should learn how to do statistical analyses in *R*, if you need a confidence interval for your standardized mean difference in the next 30 seconds, it would probably be faster (most definitely faster) to simply launch the Java application, enter your *d*, sample size, and desired confidence level and have your answer in several seconds.

Jeremy Biesanz

Associate Professor

Department of Psychology

University of British Columbia