Nursing lecture

1. Statistical Power:
Sample & Effect Size
Dr James Betts
Developing Study Skills and Research Methods (HL20107)

2. Lecture Outline:
•Statistical Power
•Effect Size
•Smallest Worthwhile Effect
•Coefficient of Variation
•Sample Size Estimation.

3. Statistical Power
• Our consideration of post-hoc tests last week
focused heavily on controlling type I error rate
• However, it is also of importance that the rate of
type II errors is controlled
• Statistical power reflects the sensitivity of our test
(i.e. the power to detect a genuine effect 80% of the time)
• So what dictates the power of a test?

5. Statistical Power
• Significance tests determine a P value based
upon 3 factors:
–
–
–
• The power of a test is therefore dictated by
the balance between the number of subjects
recruited and the Effect Size (ES).

6. ES AKA Cohen’s d
(d=D/SD)
Subject Pre-Score Post-Score Difference
Tom 12 16 4
Dick 14 17 3
Harry 10 12 2
James 12 15 3
Mean 12 15 3
SD 1.6 2.2 0.8
…BUT see Dunlap et al.
(1996) Psychological
Methods 1 (2) p. 170-7

7. ES Interpretation/Application
• Effect size shows us the magnitude of our effect
relative to SD
• Based upon the magnitude of correlation between
trials, Jacob Cohen suggests thresholds of
>0.2 (small), >0.5 (moderate) & >0.8 (large)
– (n.b. others favour >0.2, >0.6 & >1.2)
• So effect size provides a useful tool for examining
differences irrespective of sample size
• Another major application of ES is therefore to
determine the required sample size for our study.

8. GB =
38.07 s
USA =
38.08 s
Smallest Worthwhile Effect
D = 0.01 s

9. Smallest Worthwhile Effect
It would appear that even a small amount of primary
variance from an ergogenic aid would guarantee victory to
either competitor…
…however, the error variance is such that a re-run
could produce entirely different results…

10. …for an effect to guarantee first place, it would need to
exceed the opponents time by more than his error variance.
Re-Run

11. Coefficient of Variation (CV)
• The coefficient of variation expresses within subject
variation as a % of their average performance:
– e.g. USA test-retest form last 10 training sessions
• 38.06 s
• 38.08 s
• 38.07 s
• 38.11 s
• 38.09 s
• 38.07 s
• 38.10 s
• 38.05 s
• 38.08 s
• 38.09 s
Mean =
SD =
CV =

12. Smallest Worthwhile Effect
• So when conducting applied research into
performance enhancement, the smallest worthwhile
effect can be based on the actual % improvement
that produces a worthwhile increase in your chance
of winning the event
– example for 100 m sprint
an improvement of 0.3 of CV converts 2nd→1st once every ten races
Hopkins et al. (1999) MSSE 31 (3) p. 472-85
• However, we don’t always have such ecologically
valid data to support our laboratory investigations.

13. Smallest Worthwhile Effect
• Ideally, it is recommended that a pilot study is
conducted so that the typical effect size can be
established and A Priori sample size calculations
can be conducted
• Alternatively, the rationale for a planned study is
often supported by previously published literature,
in which case this data can be used as a guide to the
magnitude of effects which can be expected.

15. Sample Size Estimation
• Overall, published data can be used for A Priori power
analysis as a general guide for how many subjects to recruit
• Then post-hoc power analysis can be conducted to calculate
the actual statistical power given the sample size attained
– e.g.
“Using similar supplements to those under investigation in the
present study, van Loon et al. (2000) reported the inclusion of
protein to accelerate muscle glycogen resynthesis by 18.8
mmol glucosyl unitskg dry mass-1h-1, with a pooled standard
deviation of 6.6 mmol glucosyl unitskg dry mass-1h-1. Based
upon these data it was estimated that a sample size of 6 has a
99% power to detect such differences.

16. The purpose of sample size formulae ‘is not to
give an exact number…but rather to subject
the study design to scrutiny, including an
assessment of the validity and reliability of
data collection, and to give an estimate to
distinguish whether tens, hundreds, or
thousands of participants are required’
Williamson et al. (2000) JRSS 163: p. 10

17. Summary
• The power of a statistical test is influenced by the
size of the effect and sample size
• Effect size provides a useful tool for examining
data when sample size is small
• The smallest worthwhile effect can also be applied
to determine how many subjects would be required
for statistical significance
• Remember that our choice of data for this analysis
was very subjective in places.