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### 26 Cards in this Set

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 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 Confounding A not really the cause of B. C is instead. Temporality 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 Cross-sectional -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. Cohort -Observational & Longitudinal -Look at the "incidence" -Follow a group of "healthy" people forward in time, waiting for axe to fall. -Temorality -Non-interventional 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. Statistics 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 alpha=0.05 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 Alpha Type I, False positive -There is a statistically significant difference when there really is no difference -Type I, False Positive 1-alpha No error, true negative -Concluding that there is no statistically significant difference when there really is none -No error -True negative Beta Type II, False negative -Concluding there is no statistically significant difference when there really is a difference -Type II error -False negative 1-beta No error, true positive Concluding there is a statistically significant difference when there is a difference