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Ch 6: Making Sense of Statistical Significance: Decision Errors, Effect Size, and Power Pt 2: Sept. 26, 2013.

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Presentation on theme: "Ch 6: Making Sense of Statistical Significance: Decision Errors, Effect Size, and Power Pt 2: Sept. 26, 2013."— Presentation transcript:

1 Ch 6: Making Sense of Statistical Significance: Decision Errors, Effect Size, and Power Pt 2: Sept. 26, 2013

2 Statistical Power Probability that the study will produce a statistically significant result when the research hypothesis is in fact true – That is, what is the power to correctly reject the null? – Upper right quadrant in decision table – Want to maximize our chances that our study has the power to find a true/real result Can calculate power before the study using predictions of means – or after study using actual means

3 Statistical Power Steps for figuring power: 1. Gather the needed information: (N=16) * Mean & SD of comparison distribution (the distrib of means from Ch 5 – now known as Pop 2) * Predicted mean of experimental group (now known as Pop 1) * “Crashed” example: Pop 1 “crashed group” mean = 5.9 Pop 2 “neutral group/comparison pop” μ = 5.5,  =.8,  m = sqrt (  2 )/N  m = sqrt[(.8 2 ) / 16] =.2

4 Statistical Power 2. Figure the raw-score cutoff point on the comparison distribution to reject the null hypothesis (using Pop 2 info) For alpha =.05, 1-tailed test (remember we predicted the ‘crashed’ group would have higher fault ratings), z score cutoff = Convert z to a raw score (x) = z(  m ) + μ x = 1.64 (.2) = 5.83 Draw the distribution and cutoff point at 5.83, shade area to right of cutoff point  “critical/rejection region”

5 Statistical Power 3. Figure the Z score for this same point, but on the distribution of means for Population 1 (see ex on board) That is, convert the raw score of 5.83 to a z score using info from pop 1. – Z = (x from step 2 -  from step 1exp group )  m (from step 1 ) – (5.83 – 5.9) /.2 = -.35 – Draw another distribution & shade in everything to the right of -.35

6 Statistical Power 4.Use the normal curve table to figure the probability of getting a score higher than Z score from Step 3 Find % betw mean and z of -.35 (look up.35)… = 13.68% Add another 50% because we’re interested in area to right of mean too = 63.68%…that’s the power of the experiment.

7 Power Interpretation Our study (with N=16) has around 64% power to find a difference between the ‘crashed’ and ‘neutral’ groups if it truly exists. – Based on our estimate of what the ‘crashed’ mean will be (=5.9), so if this is incorrect, power will change. – In decision error table 1-power = beta (aka…type 2 error), so here: – Alpha? – Power? – Beta?

8 Influences on Power Main influences – effect size & N 1) Effect size – bigger d  more power – Remember formula: – Bigger difference between the 2 group means, more power to find the difference (that difference is the numerator of d) – Also, the smaller the population standard deviation, the bigger the effect size (sd is the denominator)

9 (cont.) Figuring power from predicted effect sizes – Sometimes, don’t know  1 for formula, can estimate effect size instead (use Cohen’s guidelines:.2,.5,.8 or -.2, -.5, -.8) Example:

10 Practical Ways of Increasing the Power of a Planned Study Rule of thumb: try for at least 80% power – Interpretation of 80% power – we have a.8 probability of finding an effect if one actually exists See Table 1) Try to increase effect size before the experiment (increase diffs betw 2 groups) – Training/no training group – how could you do this?

11 2) Try to decrease pop SD – use standardization so subjects in 1 group receive same instructions 3) Increase N 4) Use less stringent signif level (alpha) – but trade-off in reducing Type 1 error, so usually choose.05 or.01. 5) Use a 1-tailed test when possible


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