Categorical variable (nominal variable)May have two or more categories, like a color variable with 3 possible values: ["Red", "Blue", "Green"].
Dichotomous variableA special case of the categorical variable with only two possible values, like True/False, Yes/No, Success/Failure.
Ordinal variableA 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 variableA 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 scaleThere 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 scaleThere 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 errorRejecting a correct null assumption (H0).
Type II errorFailing to reject an incorrect null assumption (H0).
P-valueThe 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.