Data Analysis and Experimental Design

These flashcards cover key concepts related to data analysis, experimental design, and statistical methods for understanding causal relationships and hypothesis testing.

Census Survey

A survey conducted to collect data on different populations.

Randomized Control Trial (RCT)

A study design that randomly assigns participants into treatment and control groups to predict outcomes.

Binary Variable

A type of variable that can take on two possible values, such as 0 or 1.

Mean() Function

A function that calculates the mean of a set of values by dividing their sum by the number of observations.

Average Causal Effect

The overall effect of a treatment across a population, measured by comparing outcomes.

Difference in Means Estimator

A method that estimates causal effects by subtracting the mean of the control group from the mean of the treatment group.

Random Treatment Assignment

The process of randomly allocating participants to treatment or control groups to eliminate variability.

Representative Sample

A sample that accurately reflects the characteristics of the population from which it is drawn.

Standard Deviation (SD)

A measure of how spread out the values in a data set are in relation to the mean.

Linear Regression Model

A statistical method that models the relationship between two variables by fitting a linear equation.

Coefficient of Determination (R²)

A statistic that quantifies the proportion of variance in the dependent variable predictable from the independent variable.

Confounding Variables

Variables that can obscure the relationship between treatment and outcome, which should be controlled for.

Hypothesis Testing

A statistical method used to determine if there is enough evidence to reject a null hypothesis.

P-value

A measure that indicates the probability of obtaining test results at least as extreme as the observed results, under the assumption the null hypothesis is true.

Publication Bias

The tendency for journals to publish only statistically significant studies, leading to a skewed understanding of research outcomes.

Regression Discontinuity Design (RDD)

A quasi-experimental design that assigns treatment based on whether a participant's score is above or below a threshold.

Difference in Differences (DiD)

A method that compares the changes in outcomes over time between a treatment group and a control group to estimate causal effects.