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Statistical Reference Guide
Distribution
Continuous distributions
Inferences about distribution parameters
Performing a one-sample equivalence hypothesis test
Test if the mean of a distribution is equivalent to the hypothesized value.
Select a cell in the dataset.
On the
Analyse-it
ribbon tab,
in the
Statistical Analyses
group,
click
Distribution
, and then click
TOST
.
The analysis task pane opens.
In the
Y
drop-down list, select the variable.
In the
Hypothesized value
edit box, type the expected value of the parameter under the null hypothesis.
In the
Hypothesized value
edit box, type the expected value of the parameter, and then in the
Equivalence bounds
edit boxes, type the lower and upper bounds of the equivalence interval. For example, a hypothesized value of 65 with equivalence bounds of ±5. Alternatively, in the
Hypothesized value
edit box, type
-
, and then in the
Equivalence bounds
edit box, type the lower and upper limits of the equivalence interval. For example, 60 to 70.
Optional:
To compare the p-value against a predefined significance level, in the
Significance level
edit box, type the maximum probability of rejecting the null hypothesis when in fact it is true (typically 5% or 1%).
Click
Calculate
.
Related concepts
Parameter equivalence hypothesis test
Related reference
Tests for the central location parameter of a distribution
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Statistical Reference Guide
Distribution
Continuous distributions
Univariate descriptive statistics
Calculating univariate descriptive statistics
Univariate plot
Creating a univariate plot
Frequency distribution
Normality
Inferences about distribution parameters
Point and interval estimation
Parameter estimate
Estimators for the central location parameter of a distribution
Estimators for the dispersion parameter of a distribution
Estimating the parameters of a distribution
Hypothesis testing
Parameter hypothesis test
Parameter equivalence hypothesis test
Tests for the central location parameter of a distribution
Tests for the dispersion parameter of a distribution
Testing distribution parameters
Testing equivalence of distribution parameters
Discrete distributions
Study design
Compare groups
Compare pairs
Contingency tables
Correlation and association
Principal component analysis (PCA)
Factor analysis (FA)
Item reliability
Fit model
Method comparison / Agreement
Measurement systems analysis (MSA)
Reference interval
Diagnostic performance
Survival/Reliability
Control charts
Process capability
Pareto analysis
Study Designs
Bibliography
Version 6.15
Published 18-Apr-2023
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