There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.
Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?
In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:
- Is any sample greater than 30 really a “large” sample?
- Is a Chi-square test invalid if we have fewer than 5 observations in a cell?
- Does a value of .70 indicate good reliability for statistics like Kappa and Cronbach’s alpha?
- Can we rely on Cohen’s cutoffs that indicate small, medium, and large effect sizes?
- Do we really need 10 observations per independent variable in a regression model and why 10?
Note: This webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.
About the Instructor
Audrey Schnell is a statistical consultant and trainer at The Analysis Factor.
Audrey first realized her love for research and, in particular, data analysis in a career move from clinical psychology to research in dementia. As the field of genetic epidemiology and statistical genetics blossomed, Audrey moved into this emerging field and analyzed data on a wide variety of common diseases believed to have a strong genetic component including hypertension, diabetes and psychiatric disorders. She helped develop software to analyze genetic data and taught classes in the US and Europe.
Audrey has worked for Case Western Reserve University, Cedars-Sinai, University of California at San Francisco and Johns Hopkins. Audrey has a Master’s Degree in Clinical Psychology and a Ph.D. in Epidemiology and Biostatistics.
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