Summary
Highlights
Statistics is defined as the collection and interpretation of data. It is used to measure and analyze variability among individuals, such as different heights, weights, and preferences.
There are two kinds of statistics: inferential and descriptive. Inferential statistics involve analyzing a sample to make judgments about a population. Descriptive statistics focus on summarizing and explaining data, often using tools like histograms and graphs.
A population refers to the total amount of things being studied. A sample is a small part of the population used for study, and the total amount of things in a sample is called the sample size. A variable is what is being examined, which can be measurable, countable, and categorized, representing a characteristic that varies among individuals.
When measuring a variable, data can be categorical or quantitative. Quantitative data is measured in numbers that allow for arithmetic calculations (e.g., height, weight, midterm score). Categorical data places things into different groups or categories (e.g., hair color, letter grade).
Categorical variables can be ordinal or nominal. Ordinal variables have a logical ordering to their values (e.g., letter grade A-F). Nominal variables have no logical ordering (e.g., hair color).
Quantitative variables can be discrete or continuous. Discrete variables can only be measured in certain numbers (e.g., number of pets). Continuous variables can take on any numerical value (e.g., weight, which can be measured with many decimal places).
A recap of the terms: population (total things), sample (part of population), sample size (number of items in sample), and variable (what is measured). The type of variable (quantitative or categorical) depends on how it is measured. For example, midterm scores can be quantitative (actual scores) or categorical (letter grades).