Chapter 1- Introduction to Statistics

Why do we need Statistics?

To help researchers make sense of gathered information, organize and summarize data, communicate findings, and justify conclusions.

Definition of Statistics

A set of methods for organizing, summarizing, and interpreting data, providing order out of chaos.

Descriptive statistics

Methods that summarize, organize, and simplify data.

Inferential statistics

Methods that use sample data to make general statements about a population.

Population vs. Sample: Population

Set of all individuals of interest in a particular study.

Population vs. Sample: Sample

Set of individuals selected from the population intended to represent the population.

Variables

Characteristics or conditions that change or have different values across individuals.

Nominal Scale

A set of categories without a natural order (e.g., academic majors, gender).

Ordinal Scale

Categorized ranked observations (e.g., freshman, sophomore, junior, senior).

Interval Scale

Ordered categories with equal intervals but no true zero point (e.g., temperature).

Ratio Scale

Interval scale with an absolute zero point allowing for real differences (e.g., height, weight).

Discrete variables

Variables that are separate and indivisible (e.g., number of letters in your name).

Continuous variables

Variables that can be divided into an infinite number of possibilities (e.g., weight, distance).

Sampling Error

The estimated error between data from a sample and the population intended to be studied.

Independent Variable

Variable that you manipulate or categorize in a study.

Dependent Variable

Variable that you measure; its value depends on the independent variable.

Confounding Variable

Variable that systematically varies with the independent variable; should be controlled.

Constructs

Hypothetical concepts used to define behavior, needing operational definitions to be measured.

Reliability

The consistency of a measure over time.

Validity

The extent to which a test measures what it is supposed to measure.

Hypothesis Testing

The process of drawing conclusions about whether relations between variables are supported or not.

Correlational Method

Observes whether two different variables are related; correlation does NOT equal causation.

Experimental Method

Determines cause and effect by manipulating independent variables and randomly assigning participants.

Between-groups designs

Different participants complete tasks, and comparisons are made between groups.

Within-groups designs

The same participants do the tasks more than once, and comparisons are made over time.

Quasi-Experimental Method

Compares groups not created by manipulating an independent variable, determined by participant variables.