Hypotheses Null hypothesis (H0) hypothesis of no difference e.g., there is no link between disease and risk factor Alternative hypothesis (H1) hypothesis of difference e.g., there is a link between disease and risk factor Type I Error (False Positive) Stating there is an association when none exits incorrectly rejecting null hypothesis α = probability of type I error p = probability that results as or more extreme than those of the study would be observed if the null hypothesis were true general rule of thumb is that statistical significance is reached if p < 0.05 Type II Error (False Negative) Stating there is no effect when an effect exists incorrectly accepting null hypothesis β = probability of type II error Power (True Positive) Probability of correctly rejecting null hypothesis power = 1 - β Power depends on sample size increasing sample size increases power size of expected effect increasing effect size increases power True Negative Probability of correctly accepting null hypothesis Confidence Interval Range of values associated with a confidence level indicating the likelihood that the true population value of a parameter falls within that range usually done with 95% confidence interval (2 standard deviations from the mean) e.g., based on our study data, we are 95% confident that the average salary of a teacher lies between $30,000-45,000/year Confidence interval is calculated from statistics generated from the studied data Smaller confidence intervals suggest better precision of the data Larger confidence intervals suggest less precision of the data If confidence intervals of 2 groups overlap, there is no statistically significant difference A Priori Versus Post Hoc Analysis A priori comparisons comparisons planned prior to data analysis planning dependent on knowledge researchers have prior to conducting statistical tests Post hoc analysis researcher decides additional comparisons to make after viewing data choices dependent on knowledge researchers have gained after conducting statistical tests e.g., a test is run that says there is a difference between groups A, B, and C post hoc analysis would involve comparing group A to group B, B to C, and A to C to see between which groups the difference lies one potential hazard is an increased likelihood of spurious statistical associations