Confounding vs Lurking Variable

Confounding: affects both variables; Lurking: affects outcome but not considered in analysis

Chi-Square Goodness of Fit Test

Tests if sample distribution fits a population distribution

Bimodal Distribution

Distribution with two different modes

Addition Rule for Probability

P(A or B) = P(A) + P(B) - P(A and B)

Control Group

Group in an experiment that does not receive treatment

Blinding

Participants unaware of whether they are receiving treatment or placebo

Confidence Interval

Range of values believed to contain population parameter with a certain level of confidence

Cumulative Frequency

Sum of frequencies for that category and all previous categories

Binomial Distribution

Probability distribution of number of successes in a fixed number of trials

Biased vs Unbiased Estimator

Biased: systematically off; Unbiased: accurate on average

Confounding Variable

Variable related to both the treatment and the outcome

Conditional Probability Formula

P(A|B) = P(A and B)/P(B)

Conditional Probability

Probability of an event occurring given another event has already occurred

Combination

Selection of objects without regard to order

Categorical Data Analysis: Chi-square Test

Assesses relationships between categorical variables

ANOVA (Analysis of Variance)

Compares means of three or more groups

Box Plot

Displays five-number summary (minimum, Q1, median, Q3, maximum)

Correlation Coefficient

Measures strength and direction of linear relationship between two variables

Chi-Square Test for Independence

Tests if two categorical variables are independent

Alternative Hypothesis

Statement researcher wants to prove

Convenience Sampling

Use results that are easy to get

Bayes' Theorem

Calculates probability of an event based on prior knowledge of conditions

Cluster Sampling

Population divided into clusters; randomly select clusters, then sample all in cluster

Coefficient of Determination

R-squared; proportion of variance in the dependent variable predictable from the independent variable

Central Limit Theorem

Sampling distribution of the sample mean approximates a normal distribution as sample size increases