Upload
buddy-malone
View
220
Download
1
Tags:
Embed Size (px)
Citation preview
CLINICAL RESEARCH MANAGEMENT 512
Leslie McIntoshlmcintosh at path.wustl.edu
PART ITables
PART IIHypotheses RevisitedExposure and Outcomes
NOTES ABOUT HYPOTHESES
A hypothesis is a specific conjecture (statement) about a property of population.
There is a null hypothesis and an alternative (or research) hypothesis.
Researchers often expect that evidence supports the alternative hypothesis.
HYPOTHESES: POINTS TO REMEMBER
A hypothesis should be specific enough to be falsifiable
A hypothesis is a conjecture about a population (parameter), not about a sample (statistic).
A valid hypothesis is not based on the sample to be used to test the hypothesis.
2004 by Jeeshim and KUCC625
ERROR TYPES
Decision
Reject H0Do not Reject
H0
H0
TrueType I Errorα: Sig. Level
1-α: Confidence Level
False 1-β: PowerType II Error
β
H0 = Null Hypothesis
PRIMARY INTERESTS
Exposures – what affected the person intentionally (intervention) or not
Outcomes – what happened to the person Clinical measures Non-clinical measures
ACTIVITY
Exposure Outcome
ERRONEOUS CONCLUSIONS
Correlation is not equal to causation;
it is only a requirement for it.
ERRONEOUS CONCLUSIONS
Young children who sleep with the light on are much more likely to develop myopia in later life.
Published from U of Pennsylvania Medical Center in the May 13, 1999 issue of Nature, the study received much coverage at the time in the popular press.
A later study at The Ohio State University did not find a link between infants sleeping with the light on and development of myopia.
It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom
ERRONEOUS CONCLUSIONS
Correlation does not prove causation
PART IIIPower
DEFINITION OF POWER
The power of a statistical test is the probability that it will correctly lead to the rejection of a false null hypothesis (Greene 2000).
The statistical power is the ability of a test to detect an effect, if the effect actually exists (High 2000).
Statistical power is the probability that it will result in the conclusion that the phenomenon exists (Cohen 1988) .
ANALOGY TO UNDERSTAND POWER
You ask your child to find a tool in the basement. The child returns saying: “I can’t find it.”
What is the probability the tool is in the basement?
If the tool is really in the basement, what is the chance your child found it?
Hartung, 2005
CONCERNS OF POWER
Sample Size Effect Size Variability (Scatter)
Time in basement Type of tool Cleanliness of
basement
Statistics Analogy