# Statistical Glossary

• ##### Categorical variable (nominal variable)
May have two or more categories, like a color variable with 3 possible values: ["Red", "Blue", "Green"].
• ##### Dichotomous variable
A special case of the categorical variable with only two possible values, like True/False, Yes/No, Success/Failure.
• ##### Ordinal variable
A special case of a categorical variable when you may order the possible values, like the following Likert scale: Strongly disagree, Disagree, Neither agree nor disagree, Agree, Strongly agree.
• ##### Continuous variable
A numeric variable with an infinite number of values. Between any two values, you have more values. For example, between 0.01 and 0.02 you have 0.011).
• ##### Interval scale
There is a meaning for the distances between the values but not for the ratio between the values. For example, in degrees Celsius, increasing the temperature from 40°C to 60°C is double the increase from 40°C to 50°C, but 60°C is not twice as hot as 30°C.
• ##### Ratio scale
There is a meaning for the distances between the values and also for the ratio between the values. For example, a duration of 60 minutes is twice as long as a duration of 30 minutes.
• ##### Standard Error (SE)
The standard deviation of a statistic.
• ##### Standard Error of the Mean (SEM)
The standard deviation of the mean. If you know the standard deviation: SEM=σ/√n. If you estimate the standard deviation: SEM=S/√n
• ##### Type I error
Rejecting a correct null assumption (H0).
• ##### Type II error
Failing to reject an incorrect null assumption (H0).
• ##### P-value
The probability to get the sample results, or more extreme results, under the assumption that the null assumption (H0) is correct, when the p-value is very small, p-value ≤ significance level (α), you should reject the null assumption.