Data Analysis & Statistics Flashcards
6 cards from real Market Research practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Analysis & Statistics flashcards as text
What is 'regression to the mean' and why is it relevant in market research experiments?
Answer: The tendency for extreme scores to move closer to the average on subsequent measurement, potentially confounding results
Regression to the mean occurs when unusually high or low scores naturally move toward the average on retesting, which can falsely appear as a treatment effect.
What is 'structural equation modeling' (SEM) used for in market research?
Answer: Testing complex relationships among multiple latent and observed variables simultaneously
SEM allows researchers to simultaneously test complex relationships among multiple variables, integrating factor analysis and regression in one framework.
In market research, what is Net Promoter Score (NPS)?
Answer: A metric measuring customer loyalty based on likelihood to recommend, scored from -100 to +100
NPS is calculated by subtracting the percentage of detractors from promoters based on a 0-10 'likelihood to recommend' scale.
What does 'statistical power' mean in the context of market research studies?
Answer: The probability of correctly detecting a true effect when it exists
Statistical power is the probability that a study will detect an actual effect if one exists, determined by sample size, effect size, and significance level.
What is 'Bayesian analysis' in market research?
Answer: A statistical approach that updates probability estimates as new evidence is incorporated
Bayesian analysis combines prior knowledge with new data to continuously update probability estimates, allowing more flexible inference than traditional frequentist methods.
What is 'multicollinearity' and why is it a problem in regression analysis?
Answer: When independent variables are highly correlated with each other, making it difficult to isolate their individual effects
Multicollinearity occurs when independent variables are highly inter-correlated, inflating standard errors and making it difficult to determine individual variable effects.