SigmaXL Template Minimum Sample Size for Robust Hypothesis Testing

Sep 2, 2026

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This document presents a SigmaXL template designed to calculate the minimum sample size required for robust hypothesis testing. The template allows users to input parameters for a 1-sample t-test, including the hypothesis test type (e.g., 1-sample t-test), alternative hypothesis (e.g., Not Equal To), and confidence level (e.g., 95%). It also accommodates skewness and kurtosis adjustments, with coded inputs for different scenarios. The results section provides the minimum sample size (n) needed for each sample or group, with a specific example showing a required sample size of 30. The template includes regression model summaries, indicating high R-squared values (above 99%), suggesting robust model fit. It details predictor terms like 'Constant', 'Skew', and 'Kurt Delta', along with their coefficients across various model configurations. The document emphasizes the importance of sample size determination for reliable statistical inferences, especially when dealing with non-normal data distributions.

LanguageEnglish

CategoryGeneral

FormatPDF

Pages4

Reading Time2 min

AccessFree to download

Hypothesis Test

Alternative Hypothesis

Confidence Level

Description

Predictor Term

Coefficient

Regression model n:

Selected n:

Model R. Square:

Sample Data (user inputs):

Results:

Minimum sample size for each sample/group:

Coded for Regression

Notes for Regression Coding

FAQs

What is the purpose of the SigmaXL template?

The SigmaXL template is designed to calculate the minimum sample size required for robust hypothesis testing, ensuring reliable statistical inferences.

What types of hypothesis tests can be configured within the template?

The template can be configured for a 1-sample t-test, allowing for different alternative hypotheses and confidence levels to be specified.

How does the template account for non-normality in data?

The template considers skewness and kurtosis values, providing coded inputs and predictor terms like 'Skew' and 'Kurt Delta' to adjust for non-normality in the regression models.

Sep 2, 2026

Upload by Rizky