EBM - Statistics and Bias Flashcards
Selection bias
Study population does not reflect a representative sample
Recall bias
Individuals with an adverse outcome are more likely to “remember” more data, especially if a lot of time has passed
Measurement bias
Improper, inadequate, or ambiguous recording of factors
Hawthorne effect
Participants aware they are part of a study are more likely to change their behavior
Observer bias
A researcher’s belief about the effect of a treatment may be more likely to document certain outcomes
Confounding bias
A characteristic not in the causal pathway being studied can distort the effect of an exposure on the outcome.
Effect modification
The effect of an exposure differs depending on a third variable known as the modifier
Lead-time bias
Early detection can appear as a gain in survival though the course of disease has not changed
Length-time bias
Overestimation of survival duration due to the relative excess of cases detected that are slowly progressing
Quantitative (numerical) variables
Continuous and discrete
Quantitative (categorical) variables
Ordinal, nominal, and binary
Power
The probability of detecting a difference; 1 - beta
Type I error (alpha)
Rejecting the null hypothesis when it is actually true
Type II error (beta)
Accepting the null hypothesis when it is actually false
Parametric tests
Typically require a normal distribution and assume equal variance between groups
Examples: t-tests, ANOVA, Pearson’s correlation, linear regression
