Binomial Distribution
Probability distribution of number of successes in a fixed number of trials
Chi-Square Test for Independence
Tests if two categorical variables are independent
Bimodal Distribution
Distribution with two different modes
Central Limit Theorem
Sampling distribution of the sample mean approximates a normal distribution as sample size increases
Box Plot
Displays five-number summary (minimum, Q1, median, Q3, maximum)
Coefficient of Determination
R-squared; proportion of variance in the dependent variable predictable from the independent variable
Alternative Hypothesis
Statement researcher wants to prove
ANOVA (Analysis of Variance)
Compares means of three or more groups
Cluster Sampling
Population divided into clusters; randomly select clusters, then sample all in cluster
Cumulative Frequency
Sum of frequencies for that category and all previous categories
Combination
Selection of objects without regard to order
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
Addition Rule for Probability
P(A or B) = P(A) + P(B) - P(A and B)
Convenience Sampling
Use results that are easy to get
Correlation Coefficient
Measures strength and direction of linear relationship between two variables
Categorical Data Analysis: Chi-square Test
Assesses relationships between categorical variables
Conditional Probability
Probability of an event occurring given another event has already occurred
Chi-Square Goodness of Fit Test
Tests if sample distribution fits a population distribution
Bayes' Theorem
Calculates probability of an event based on prior knowledge of conditions
Control Group
Group in an experiment that does not receive treatment
Confounding vs Lurking Variable
Confounding: affects both variables; Lurking: affects outcome but not considered in analysis
Biased vs Unbiased Estimator
Biased: systematically off; Unbiased: accurate on average
Conditional Probability Formula
P(A|B) = P(A and B)/P(B)
Confounding Variable
Variable related to both the treatment and the outcome