Of course, you could continue to add additional parameters that affect power, such as the relative efficiency of the estimator that you employ, the correlations between samples in nonindependent samples designs, the exact shape of the distributions from which the samples were drawn, the reliability of your measurements, and so on. ----- Original Message ----- From: "Christopher D. Green" <[EMAIL PROTECTED]> To: "Teaching in the Psychological Sciences" <[EMAIL PROTECTED]> Sent: Tuesday, November 12, 2002 4:58 PM Subject: [Fwd: SPSS & Power]
Earlier today I wrote: > Power is a funtion of two independent components: effects size and sample size > (suitably adjusted depending on the design). This was, of course, not quite correct. Power is a function of *three* independent components: effect size, sample size, and *alpha* (the probability of making a type I error). Effect size and sample size usually turn up explicitly in the formula. Alpha usually turns up only in the table of non-central F that is used. Sorry for the inexactitude. Regards, -- Christopher D. Green Department of Psychology York University Toronto, Ontario, Canada M3J 1P3 --- You are currently subscribed to tips as: [EMAIL PROTECTED] To unsubscribe send a blank email to [EMAIL PROTECTED]
