To infer generalizations about a population based on sample data.

To describe the data by using standard methods to determine the average value, the range of data around the average, and other characteristics.

To identify causal relationships between variables.

To perform hypothesis testing on a dataset.

To assess the variability within a dataset without making generalizations.

To calculate the average and spread of data within a sample.

To infer the likelihood that the observed results can be generalized to other samples of individuals.

To rank data points on a scale for visual representation.

A fixed characteristic that is identical across a population.

A measure of central tendency in a dataset.

A characteristic of interest for each person or thing in a population that varies and can be measured.

A type of graph used to summarize numerical data.

Interval and ordinal measurement.

Continuous and discrete measurement.

Score/numerical assessment and nominal/categorical measurement.

Dichotomous and ratio measurement.

Measurements that can have any number of values within a range.

These take only specific, distinct values, with no intermediate values between them (e.g., 1, 2, 3; but not 2.34).

Data that is always represented using bar charts.

Values with constant quantitative relationships, but not true zero.

Measurements that are constrained to a small set of fixed values.

Measurements that involve categories rather than amounts.

Measurements that can take any value within a given range and may have infinite precision (e.g., 4.303).

Measurements used only for nominal data.

Continuous variables involve counting whilst discrete variables involve measurement.

Continuous variables involve counting whilst discrete variables involve ranking.

Discrete variables involve counting whilst continuous variables involve measurement.

Continuous and discrete variables are types of ordinal data.

Data ranked along a continuum so that different values represent the relative magnitude of responses.

Data with a specific and constant quantitative relationship between scores or responses.

Data that can be compared as ratios as it possesses a true zero.

Data that differs in type rather than amount.

Data that differs in type rather than amount.

Data ranked along a continuum so that different values represent the relative magnitude of responses.

Data with equal intervals between values but not true zero point.

Data used for bar charts and pie charts exclusively.

Data that can only take specific, distinct values (e.g., 1, 2, 3).

Data with a specific and constant quantitative relationship between the scores or responses.

Data ranked along a continuum without consistent intervals.

Data that possesses a true zero and can be compared as ratios.

Data ranked along a continuum so that different values represent relative magnitude.

Data that can be compared as ratios as it possesses a true zero.

Data with equal intervals but not true zero.

Data that differs in type rather than amount.

Histograms and polygons.

Bar charts and pie charts.

Scatter plots and box plots.

Line graphs and stem-and-leaf plots.

Histograms and polygons.

Bar charts and pie charts.

Frequency tables and scatter plots.

Line graphs and stem-and-leaf plots.

Mean and median.

Variance and standard deviation.

Mode and frequency counts.

Interquartile range and percentile ranks.

A nominal variable.

A dichotomous variable.

A continuous variable.

An ordinal variable.

Ordinal and Interval

Ratio and Ordinal

Interval and Ratio

Ordinal and Interval

Nominal

Ordinal

Interval

Ratio

Nominal

Ordinal

Interval

Ratio