Statistics Cheat Sheet

Random Sample: each member of the population has the same chance of being selected

Representative Sample: characteristics should represent those of the target population without bias

Observational Study: no intervention by the investigator, no treatment imposed

Experimental Study: investigator has some control over the determinant

Variables:
Categorical – each observation falls into a finite number of groups
Nominal: named variables with no implied order e.g. personality type
Ordinal: grouped variables with implied order e.g. level of education
Continuous – measured variables
Discrete: take discrete values e.g. number of children
Numerical: can assume any value within a certain range/interval e.g. height

Types of Designs:
True experiment: researcher has potential to randomly allocate observations to conditions
Quasi-experiment: demonstrate a relationship between an IV/DV researcher makes use of naturally occurring groups, cant make cause and effect statements
Non-experiments (correlation design): question if there is a relationship between variables, cant make cause & effect statements

Between groups: two groups being compared on some outcome measure

Within-subjects: participants experience each condition of an IV, with measurements of some outcome taken on each occasion

Extraneous variables: variable present in an experiment, which might interfere with the relationship between IV & DV

Confounding variables: mediating variable that can adversely affect the relation between IV/DV

Internal validity: extent to which a casual relationship can be assumed between IV & DV. External validity: degree to which you can generalize the results of your study to some underlying population

T-test

One sample t-test – A: data should arise from a normal population
Paired t-test – A: differences should arise from a normal population
Two sample t-test – A: samples must be independent, arise from a normal distribution &