Hypothesis Testing for Normality vs. Means: A Python Comparison

Hypothesis Testing for Normality vs. Means: A Python Comparison

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Hypothesis testing is a fundamental statistical technique used to determine if a sample arriving from a certain population exhibits a significant deviation from a hypothesized distribution or a known population mean. In this description, we will explore the application of hypothesis testing in assessing normality and comparing population means using Python. local analytical functions within NumPy and Scipy.

First, we'll focus on hypothesis tests for normality. The Shapiro-Wilk and Anderson Darling normality tests are both widely used tests for assessing the normality of a dataset. This analysis will include identifying potential deviations from normality, understanding how p-values are calculated, and determining the appropriate actions based on the test results.

Subsequently, we will demonstrate the application of hypothesis testing for means, focusing on the Student's t-test and One-Way ANOVA (Analysis of Variance). These tests enable comparisons between the means of two or more groups, providing insights into the potential significance of observed differences. As a part of this analysis, we will discuss the underlying assumptions required for accurate testing and calculate sample effect sizes for meaningful interpretation.

Upon completing this content, you will be well-versed in the intricacies of hypothesis testing for normality and population means using Python, empowering you to gain insights into your data with confidence.


Additional Resources:
1. Shapiro-Wilk test for Normality: https://docs.scipy.org/doc/scipy/refe...
2. Anderson-Darling test for Normality: https://docs.scipy.org/doc/scipy/refe...

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