#### II. Definition: Statistical Significance

1. Statistical Significance is the probability that study findings are due to chance
1. The risk of rejecting the null hypothesis when it is actually true
2. In planning a study, a level of risk is chosen (e.g. 5% risk or P-Value of 0.05)
2. P-Value is used to denote significance
1. P-Value < 0.05: <5% that findings due to chance
2. Reflects reproducibility of the study findings only
3. Does not predict individual patient's effect
3. Confidence Interval
1. Provides a range of possible outcomes
2. Example: Number Needed to Treat ranges from 20 to 100
3. More clinically relevant
4. Statistical Significance does not mean clinically useful
1. See Clinical Significance below

#### III. Definition: Clinical Significance

1. Reflects how much of an effect a patient sees
2. Example:
1. Study shows drug x significantly improves Hair Growth
2. Reality: Even the patient cannot see the difference

#### IV. Definition: Heterogeneity

1. Step 1: Clinical Heterogeneity (study similarity)
1. Assess similarity of who and what was evaluated across pooled, meta-analyzed studies
2. Evaluates similarity between studies (systematic review, meta-analysis)
3. Combined studies in meta-analysis should have similar protocols (e.g. inclusion and exclusion criteria)
2. Step 2: Statistical Heterogeneity
1. Assess similarity among study results (were they consistent)
2. Assign a P-Value to a combined group of studies that reflects the difference in their results and the likelihood that this is due to random chance
3. Large spread of data across more than one study (i.e. greater heterogeneity) suggests the studies should not be combined in meta-analysis
3. References
1. Newman in Majoewsky (2013) EM:Rap 13(7): 7