Type I error – rejects null hypothesis incorrectly → falsely assumed there was a difference when no difference exists
Type II error – accepts null hypothesis incorrectly because of small sample size → the treatments are interpreted as equal when there is actually a difference
Null hypothesis – hypothesis that no difference exists between groups
p < 0.05 rejects the null hypothesis
• p < 0.05 = > 95% likelihood that the difference between the populations is true
• < 5% likelihood that the difference is not true and occurred by chance alone
Variance – spread of data around a mean
Parameter – population
Numeric terms – example: 2, 7, 7, 8, 9, 11, 15
• Mode – most frequently occurring value = 7
• Mean – average = 9
• Median – middle value of a set of data (50th percentile) = 8
TRIALS AND STUDIES
Randomized controlled trial – prospective study with random assignment to treatment and nontreatment groups
• Avoids treatment biases
Double-blind controlled trial – prospective study in which patient and doctor are blind to the treatment
• Avoids observational biases
Cohort study – prospective study → compares disease rate between exposed and unexposed groups (nonrandom assignment)
Case-control study – retrospective study in which those who have the disease are compared with a similar population who do not have the disease; the frequency of the suspected risk factor is then compared between the 2 groups
Meta-analysis – combining data from different studies
QUANTITATIVE VARIABLES
Student’s t test – 2 independent groups and variable is quantitative → compares means (mean weight between 2 groups)
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