Qualitative and quantitative

Discrete and continuous only

Categorical (nominal and ordinal) and numerical (interval and ratio)

Descriptive and inferential

Probability measures the likelihood that a particular event will occur, expressed as a number between 0 and 1.

Probability is the precise prediction of future events.

Probability is the part of statistics that deals with qualitative data.

Probability provides absolute certainty in outcomes.

Discrete variables can have any value within a range, while continuous have finite distinct values.

Discrete variables have a finite number of distinct values, while continuous variables can take any value within a range.

Discrete variables have a set pattern, continuous variables do not.

Discrete variables are unmeasurable, continuous variables are based on whole numbers only.

Statistical significance is the probability of the null hypothesis being correct.

Statistical significance determines the importance of a statistical variable.

Statistical significance is the point at which all data agrees perfectly.

Statistical significance assesses whether results are likely due to chance or if there is a genuine effect.

A p-value is the threshold for determining if a result is accurate.

A p-value measures the probability of obtaining a result at least as extreme as the observed results, assuming that the null hypothesis is true.

A p-value guarantees the result's legitimacy.

A p-value is a fixed statistic used across all tests.