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26 Cards in this Set

  • Front
  • Back
Prevalence of B
Proportion of a population that has B at a point in time
Incidence of B
Rate of NEW cases of B that appear in a population
A not really the cause of B. C is instead.
Cause precedes effect
Case Report
-Observational study of an individual
-Will be at least one case where A is associated with B.
Case Series
-Observational study of a group
-Observational study at one point in time
-Studies "prevalence"
-Pros: easy to conduct
-Cons: No temporality
Case Control
-Observational & Retrospective
-Start with people who have the disease and go back and look at what they have been previously exposed to.
-Observational & Longitudinal
-Look at the "incidence"
-Follow a group of "healthy" people forward in time, waiting for axe to fall.
Randomized, control trial
-Interventional & longitudinal
-Look at the "incidence"
-NOT susceptible to confounding
-temporality established
The only study that is not susceptible to confounding?
Randomized, controlled trial
Studies that establishes temporality?
Cohort & randomized control
Study that may have ethical issues?
Randomized controlled trial
Null hypothesis (Ho)
The predicted difference does not exist
Alternative hypothesis (H1)
The predicted difference does exist.
Give us an estimate of the probability that a particular observed difference occurred by chance alone.
What factors affect probability (p)
1. Size of observed difference (bigger size, lower p)
2. # of subjects in the sample population (more subjects, lower p)
3. Variability of outcome (more consistent difference, lower p)
4. lower p means less probability of getting a result by chance; so better chance that our hypothesis is right.
If p=0.001
.1% probability of finding this difference in the sample alone
Reject the null hypothesis
If p=0.31
Accept the null hypothesis
alpha = tolerance for error
if p less than alpha
-Difference is statistically significant
-We accept H1
If p is greater than alpha
-Difference is not statistically significant
-We accept Ho
Type I, False positive
-There is a statistically significant difference when there really is no difference
-Type I, False Positive
No error, true negative
-Concluding that there is no statistically significant difference when there really is none
-No error
-True negative
Type II, False negative
-Concluding there is no statistically significant difference when there really is a difference
-Type II error
-False negative
No error, true positive
Concluding there is a statistically significant difference when there is a difference