α (‘Alpha’)
The probability of committing a Type I error. Also known as the significance level.
Analysis of Variance (ANOVA)
A statistical method that analyzes variances to determine if the means from more than two populations are the same.
Bar Chart
A graph where the height of the bar for each category is equal to the frequency (number of observations) in the category.
Bayes’ Theorem
A theorem stating that if (A_1, \dots, A_k) are (k) mutually exclusive and exhaustive events, then (P(A_{i}
β (‘Beta’)
The probability of committing a Type II error.
Binary Categorical Variable
A variable that has two possible outcomes.
Binomial Distribution
A special discrete distribution where there are two distinct complementary outcomes, a “success” and a “failure.” It applies when an experiment consists of a fixed number of independent, identical trials, each with the same probability of success.
Bootstrapping
A method of using samples to find the approximate sampling distribution of a statistic.
Classical Interpretation of Probability
The probability that event E occurs is denoted by P(E). When all outcomes are equally likely, then: (P(E) = \frac{\text{number of outcomes in E}}{\text{number of possible outcomes}}).
Coefficient of Variation (CV)
A unit-free statistic used to compare dispersion of data from two or more distinct populations, calculated as (CV = \dfrac{\text{Standard Deviation}}{\text{Mean}}).
Conditional Probability
The probability of one event occurring given that it is known that a second event has occurred.
Confounding variable
A variable that is in the study and is related to the other study variables, thus having an effect on the relationship between these variables.
Control Group
The group that did not receive the study treatment(s) and is used as a benchmark.
Critical values
The values that separate the rejection and non-rejection regions in a hypothesis test.
Dependent Events
Two events are not independent if the knowledge of the outcome of one changes the probability of the other.
Descriptive statistics
Techniques of describing data in ways to capture the essence of the information in the data.
Empirical Rule
In any normal or bell-shaped distribution, roughly 68% of observations lie within one standard deviation of the mean, 95% within two, and 99.7% within three.
Experimental (study)
A study that involves some random assignment of a treatment; researchers can draw cause and effect (or causal) conclusions.
Explanatory Variable
Variables that serve to explain changes in the response. They may also be called the predictor or independent variables.
Factors and Measurements
The factors are the controlled categorical predictors in the study. The response which is recorded but not controlled by the researcher is sometimes called measurements.
False Negatives
When test results come back negative for someone who is actually positive.
False Positives
When test results come back positive for someone who is actually negative.
Independent Events
Two events, A and B, are considered independent if the probability of A occurring is not changed based on any knowledge of the outcome of B.
Inferential statistics
The process of drawing conclusions from data about the population.
