Categorical variable (nominal variable)May have two or more categories, like a color variable with 3 possible values: ["Red", "Blue", "Green"].
Confidence levelThe certainty level that the true value of the estimated parameter will be in the confidence interval.
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.
CDFComulative distribution function, F(x)=P(X≤x).
MeanThe mean is a value that represents the middle of a set of numbers.
Usually, the mean refers to to the arithmetic mean or arithmetic average.
Sample datausually we don't have the data of the entire population, a random sample data represent the entire population.
Sample averageThe average of the sample data.
Standard deviation (σ)The standard deviation is a statistic that measures the data variability. It is derived from the square root of the distances between each value in the population and the population's mean squared.
Sample standard deviation (S)The standard deviation of the sample data, the calculation is the same as for the standard deviation, but to get an unbias estimation of the standard deviation, the division is by (n - 1) instead of n.
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).
PowerThe statistical power is the probability that a test will reject an incorrect H0 for a defined effect size. Researchers usually use a priori power of 0.8. More
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.
Significance level (α)The maximum chance allowed rejecting H0 while H0 is correct, the maximum probability of Type1 Error. significance level of 0.05 is commonly used.
EffectWhen you choose the Effect the tool determines the Effect type and the Effect size. Ignore this field if you know the required Effect type and the Effect size. If you do not know what to do, use Medium effect
Effect SizeThe expected effect that the test should detect.
Any change in Effect will change this value!
You may override this value.
RoundingWhen the number is bigger than one the calculator rounds to the required decimal places, but when the number is smaller than one, it rounds to the required significant figures
For example, when you choose 2, it will format 88.1234 to 88.12 , and 0.001234 to 0.0012.