Research & Evidence-Based Practice Flashcards
7 cards from real Insurance practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Research & Evidence-Based Practice flashcards as text
In insurance research, what does a 'confidence interval' represent?
Answer: The range within which the true population parameter likely falls
A confidence interval defines the range where the true population value is expected to fall with a specified level of certainty (e.g., 95%).
Which study design is considered the gold standard for establishing causality in insurance outcome research?
Answer: Randomized controlled trial
Randomized controlled trials minimize confounding by randomly assigning subjects to treatment and control groups, establishing the strongest causal evidence.
An insurer uses a loss development triangle to estimate future claim payments. What is the primary purpose of this actuarial tool?
Answer: To project ultimate losses from immature claim data
Loss development triangles show how reported claims mature over time, allowing actuaries to estimate incurred but not yet fully developed losses.
What is 'selection bias' in the context of insurance research?
Answer: Systematic error from non-random differences between study and comparison groups
Selection bias occurs when the study sample is not representative of the population, leading to skewed or invalid conclusions.
A meta-analysis in insurance research primarily serves to:
Answer: Pool and synthesize results from multiple independent studies
Meta-analysis statistically combines findings from several studies to produce a more precise overall estimate of an effect.
Which metric best measures the predictive accuracy of a binary insurance underwriting model?
Answer: Area Under the ROC Curve (AUC)
The AUC (ROC curve area) quantifies how well a binary classifier distinguishes between two outcomes, making it standard for evaluating underwriting models.
When an insurance researcher reports a p-value of 0.03, it means:
Answer: There is a 3% probability of observing results this extreme if the null hypothesis is true
A p-value of 0.03 means there is only a 3% probability of obtaining the observed results (or more extreme) if the null hypothesis were true.