# PARAMETRIC HYPOTHESIS TESTS

• Parametric One Variable (T) Mean.The one-variable t-test of means is appropriate when the population standard deviation is not known but the sampling distribution is assumed to be approximately normal (the t-test is used when the sample size is less than 30). This t-test can be applied to three types of hypothesis tests—a two-tailed test, a right-tailed test, and a left-tailed test—to examine if the population mean is equal to, less than, or greater than the hypothesized mean based on the sample dataset.
• Short Tip: Runs a one-variable T-test for means (H0: the population mean is statistically equal to the hypothesized mean).
• Model Input: Data Type A. One input variable is required with at least 5 rows of data.
• Variable:
• >VAR1
• Parametric One Variable (Z) Mean. The one-variable Z-test is appropriate when the population standard deviation is known, and the sampling distribution is assumed to be approximately normal (this applies when the number of data points exceeds 30).
• Short Tip: Runs a one variable Z-test for means (H0: the population mean is statistically equal to the hypothesized mean).
• Model Input: Data Type A. One input variable is required with at least 5 rows of data.
• Variable:
• >VAR1
• Parametric One-Variable (Z) Proportion. The one-variable Z-test for proportions is appropriate when the sampling distribution is assumed to be approximately normal (this applies when the number of data points exceeds 30, and when the number of data points, N, multiplied by the hypothesized population proportion mean, P, is greater than or equal to 5, NP ≥ 5). The data used in the analysis must be proportions and be between 0 and 1.
• Short Tip: Runs a one variable Z-test for proportions (H0: the population proportion is statistically equal to the hypothesized mean).
• Model Input: Data Type A. One input variable is required with at least 5 rows of data.
• Variable:
• >VAR1
• Parametric: Power Curve for T-Test.Beta is the acceptable level of Type II error (the probability that the null hypothesis is not rejected when it is false) and power is 1 – Beta.
• Short Tip: Computes the Beta and Power of a single variable test.
• Model Input: Data Type A. One input variable is required with at least 5 rows of data. Hypothesized mean can be any numerical value, and the Alpha level must be a positive input (typically 0.01, 0.05, or 0.10).
• Data Variable, Hypothesized Mean, Alpha:
• >VAR1
• >50
• >0.05
• Parametric Two-Variable (F) Variances. The two-variable F-test analyzes the variances from two samples (the population variance of Sample 1 is tested with the population variance of Sample 2 to see if they are equal) and is appropriate when the population standard deviation is not known but the sampling distribution is assumed to be approximately normal.
• Short Tip: Tests if the variances of two variables are equal (H0: the two variables’ variances are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2:
• >VAR1; VAR2
• Parametric Two-Variable (T) Dependent Mean. The two-variable dependent t-testis appropriate when the population standard deviation is not known but the sampling distribution is assumed to be approximately normal (the t-test is used when the sample size is less than 30). In addition, this test is specifically formulated for testing the same or similar samples before and after an event (e.g., measurements taken before a medical treatment are compared against those measurements taken after the treatment to see if there is a difference).
• Short Tip: Tests if the means of two variables are equal when the variables are dependent (H0: the two variables’ means are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2:
• >VAR1; VAR2
• Parametric Two-Variable (T) Independent Equal Variance. The two-variable t-test with equal variances is appropriate when the population standard deviation is not known but the sampling distribution is assumed to be approximately normal (the t-test is used when the sample size is less than 30). In addition, the two independent samples are assumed to have similar variances.
• Short Tip: Tests if the means are equal for two independent equal variance variables (H0: the two variables’ means are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2:
• >VAR1; VAR2
• Parametric Two-Variable (T) Independent Unequal Variance. The two-variable t-test with unequal variances (the population variance of sample 1 is expected to be different from the population variance of sample 2) is appropriate when the population standard deviation is not known but the sampling distribution is assumed to be approximately normal (the t-test is used when the sample size is less than 30). Also, the two independent samples are assumed to have similar variances.
• Short Tip: Tests if the means are equal for two independent unequal variance variables (H0: the two variables’ means are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2
• >VAR1; VAR2
• Parametric Two-Variable (Z) Independent Means. The two-variable Z-test is appropriate when the population standard deviations are known for the two samples and the sampling distribution of each variable is assumed to be approximately normal (when the number of data points of each variable exceeds 30).
• Short Tip: Tests if the means are equal for two independent variables with known variances (H0: the two variables’ means are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2, Hypothesized Mean Difference, Stdev 1, Stdev 2
• >VAR1; VAR2
• >5
• >123.45
• >87.6
• Parametric Two-Variable (Z) Independent Proportions. The two-variable Z-test on proportions is appropriate when the sampling distribution is assumed to be approximately normal (this applies when the number of data points of both samples exceeds 30). Further, the data should all be proportions and be between 0 and 1.
• Short Tip: Tests if the proportions are equal for two independent variables (H0: the two variables’ proportions are equal).
• Model Input: Data Type B. Two input variables are required with at least 5 rows of data each.
• Variable 1, Variable 2, Hypothesized Mean Difference:
• >VAR1; VAR2
• >5
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