User:Jtneill/Presentations/More complex summary statistics

Dr. James Neill, Assistant Professor, Centre for Applied Psychology, Faculty of Health, University of Canberra Statistics Networking Day, 6 August, 2015, UC

What's your favourite complex summary statistic?

 * 1) What would you choose?
 * 2) For me the choice is easy - although I wasn't taught how to use effect sizes in undergraduate psychology, it is the statistic that I use the most and find most useful.

My favourite: Effect sizes

 * 1) Social sciences have tended to over-emphasise null hypothesis significance testing and under-emphasise use of effect sizes:  "I believe that the almost universal reliance on merely refuting the null hypothesis as the standard method for corroborating substantive theories in the soft areas is a terrible mistake, is basically unsound, poor scientific strategy, and one of the worst things that ever happened in the history of psychology" (Meehl, 1978, p. 817)
 * 2) Statistical significance is a function of effect size, sample size, and probability level - but most often, people want to know about the effect size (i.e., "how strong is the relationship or how big is the difference?).
 * 3) Effect sizes can be re-expressed as other effect sizes or other common language formats such as percentages.

Confidence intervals

 * 1) Accompanying ESs with CIs offers the best of both worlds - i.e., indicates the size of an observed effect and uncertainty



Take-home message

 * 1) In applied social science research, people generally want to know about the strength of relationship or the size of the difference - so, report effect sizes. Don't rely solely on inferential statistical testing.
 * 2) Graph effect sizes and include confidence intervals.