Statistical Test Explorer

Every test the statistical test guidance knows, all at once. Answer the questions in any order and the tests that no longer fit fade out. The number beside each answer is how many tests would remain if you chose it.

Your study

Clear answers

Type of study

Describe data for group4 Compare group with hypothetical value or distribution0 Compare two or more groups0 Study repeated measures designs0 Study association between variables0

Number of measurements*

One0 Two0 Two or more0

Independent variable

Number of variables*

None0 One2 Two7 Two or more7

Scale*

N/A– Nominal– Ordinal– Interval– Ratio–

Set by IV number: None.

Number of levels

N/A– Two– Two or more– Many–

Set by IV number: None.

Dependent variable

Number of variables*

One4 Two0 Two or more0

Scale*

N/A0 Nominal0 Ordinal0 Interval0 Ratio0

Number of levels

Two0 Two or more0 Many0

What do these questions mean?

Type of study

This tool distinguishes between 5 major types of designs that may apply to a research question.

Describe data for groups: choose this design when the RQ only asks for a summary of the data (e.g., mean or median).
Compare group with hypothetical value or distribution: choose this design when the RQ asks about how the data collected for a group of subjects compares to a value generated by different means (e.g., previous research, a theory) or if it asks if the data follows a certain distribution (e.g., normal distribution).
Compare two or more groups: choose this design when the RQ asks how the data collected for identifiable, separate groups of subjects compare between these groups (e.g., in experimental design between control and treatment groups). Use for causal inferences.
Study repeated measures designs: choose when the RQ asks how the same group of participants compares across measurement occasions for one or more measures (e.g., pre-post or time series designs). Can be used for causal inferences.
Study associations between variables: choose when the RQ asks if a pattern of association that may exist between two or more variables is due to chance or not (e.g., one variable grows with the growth of another variable), but no information about causality is sought.

Number of measurements

The number of measurements question asks about how many times the same group of subjects have been measured. The options are:

One: only once (e.g., cross-sectional studies)
Two: exactly two times (e.g., pre-post studies)
Two or more: at least two times but can be more (e.g., time series)

Number of variables

How many independent (IV) or dependent (DV) variables are being included in the RQ. To note that there may be RQs that do not have independent variables.

None: available only for the independent variables; choose in case the RQ does not have an IV.
One: choose if the RQ has one IV only;
Two: choose if the RQ includes exactly two IVs./
Two or more: choose if the RQ includes at least two IVs.

Scale

Choose how the RQ variable(s) (either IV or DV) are measured. The four scales provide increasingly more information about the data (e.g., the ordinal scale offers more information than the nominal scale). If the RQ includes more than one variable, chose select the scale offering the least amount of information.

In order:

Nominal: classifies unordered, mutually exclusive categories (e.g., gender as male or female). Offers the least amount of information about the data.
Ordinal: ranking hierarchy of levels (e.g., letter grades). Provides more information that a nominal variable by offering a ranking (order) of the levels, following a specific, known rule.
Interval: improves on the ordinal scale by adding equal differences between the levels, with real life equal meaning or counterpart, with one downside, it has no true zero point (e.g., the Celsius scale has a value of 0, but that value does not mean there is no heat present).
Ratio: similar to interval scales, but with a true zero point (e.g., the system of inches), which gives these scales the ability to reason about ratios between scale values.

Number of Levels

The number of levels for the variable(s), IV or DV. If the RQ has more than one variable, choose the option matching the variable with the highest number of levels.

N/A: choose when the number of levels are not relevant (e.g., if the RQ does not have an IV).
Two: chose when the variable has exactly two levels (e.g., a variable that has only yes/no answers).
Two or more: choose when the variable has two or more levels (e.g., letter grades).
Many: choose when a variable has many levels (e.g., a scale between 0 and 100 has 100 levels).

0 of 46 tests fit No test fits these answers

Point-Biserial Correlation

Used to measure the relationship between a dichotomous variable and a variable measured on an interval or ratio scale. It is the Pearson correlation computed with the two categories coded 0 and 1, and its test is the same as Student's (equal-variance) independent samples t-Test, not Welch's version. When the dichotomy was created by cutting a continuous variable in two, such as pass and fail on an exam, the biserial correlation is used instead.

Levels: Favored when one of the variables has only two levels.

Test card, explanations, and examples → (opens in a new tab)

Faded: IV number of variables is None, which this test does not fit. IV scale is N/A (set by IV number: None), which this test does not fit. IV number of levels is N/A (set by IV number: None), which this test does not fit. DV number of variables is Two or more, which this test does not fit.

Number of measurements: One
IV number of variables: One
IV scale: Nominal
IV number of levels: Two
DV number of variables: One
DV scale: Interval, Ratio
DV number of levels: Many

Descriptive statistics 4

Statistical tests 42

No description yet. Select a test to see what it needs and why it faded.