Making Sense of Statistical
Making Sense of Statistical
Significance
Significance
Decision Errors, Effect Size, and Statistical Power
Decision Errors, Effect Size, and Statistical Power
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Understanding Statistical Significance, Effect Size, and Statistical Power in Research, Slides of Statistics
An overview of statistical significance, decision errors, effect size, and statistical power. It explains the concepts of type i and type ii errors, the importance of setting the right significance level, and the role of effect size and statistical power in interpreting research results. It also includes illustrations and examples to help understand these concepts.
Typology: Slides
2020/2021
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Making Sense of StatisticalMaking Sense of Statistical
Significance Significance
Decision Errors, Effect Size, and Statistical Power Decision Errors, Effect Size, and Statistical Power
Decision Errors
When the right procedure leads to the wrong
conclusion
Type I Error
- (^) Reject the null hypothesis when it is true
- (^) Conclude that a manipulation had an effect when in fact it did not
Type II Error
- (^) Fail to reject the null when it is false
- (^) Conclude that a manipulation did not have an effect when in fact it did
Effect Size
Amount that two populations do not overlap
- (^) The extent to which the experimental procedure had the effect of separating the two groups
- (^) Calculated by dividing the difference between the two population means by the population standard deviation
- (^) Effect size conventions
- (^) Small =.
- (^) Medium =.
- (^) Large =.
Effect Size Conventions
Pairs of population
distributions showing
a. Small effect size b. Medium effect size c. Large effect size
Statistical Power
- (^) Probability that a study will produce a statistically
significant result if the research hypothesis is true
Can be determined from power tables
- (^) Depends primarily on effect size and sample size
- (^) More power if…
- (^) Bigger difference between means
- (^) Smaller population standard deviation
- (^) More people in the study
- (^) More power if…
- (^) Also affected by significance level, one- vs. two-tailed
tests, and type of hypothesis-testing procedure used
Statistical Power
Two distributions
may have little
overlap, and the
study high power,
because
a. The two means are very different b. The variance is very small
Role of Power in Interpreting
the Results of a Study
When result is significant
- (^) If sample size small, effect is probably practically significant as well
- (^) If sample size large, effect may be too small to be useful
When result is insignificant
- (^) If sample size small, study is inconclusive
- If sample size large, research hypothesis probably false