Answer Strategy

Statistical or qualitative methods to summarise and interpret data (cleaning, transforming, estimating data.)

Domain Unit Outcomes Conditions Uncertainty

Bayesian: Focuses on the probability of an estimate being the true value (estimand). Frequentist: Focuses on procedures for arriving at an estimate.

• Point Estimation • Hypothesis Testing • Bayesian Formulations • Interval Estimation • Extreme Bounds

Provides a single value as an estimate of a population parameter (e.g., mean, regression coefficient). This estimate can be derived using techniques like maximum likelihood estimation. Uncertainty: standard error

Hypothesis testing is a statistical method using a null hypothesis to assess the likelihood of observing an outcome as extreme as the one observed, assuming the null hypothesis is true.

P-value: Probability of getting an estimate as extreme as ours if there’s truly no effect p > 0.1 = statistically insignificant p < 0.05 = statistically significant p < 0.001 = statistically highly significant

Type I Error (False Positive): Rejecting a true null hypothesis. Type II Error (False Negative): Failing to reject a false null hypothesis.

The Bayesian formulation is a statistical method that combines prior beliefs with data to generate probabilities for a range of possible answers/estimands. It aims to generate “rational beliefs” about the values you’re looking for

Provides a range of values where the true value is likely to lie with a certain level of confidence

Estimates the “best” and “worst” case scenarios based on the data.