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Statistical Hypothesis Testing is a method used in to make inferences or draw conclusions about a population characteristic based on a sample of data.
It involves formulating Null Hypothesis (H₀) and Alternative Hypothesis (H₁/Ha).
The purpose of hypothesis testing is to determine whether the evidence from the data is strong enough to reject the null hypothesis in favor of the alternative hypothesis.
The process involves Choosing a Significance Level (α), Calculating a Test Statistic based on the sample data and Drawing Conclusions about the Null Hypothesis.
Hypothesis testing is widely used in various fields like science, engineering, and business to validate claims, assess theories, and support decision-making.
Training Program on Statistical Hypothesis Testing :
1. Introduction to Hypothesis Testing - Overview of the purpose and importance of hypothesis testing in data analysis.
1. Introduction to Hypothesis Testing - Overview of the purpose and importance of hypothesis testing in data analysis.
2. Key Concepts and Terminology -
a. Hypotheses Definition - Null Hypothesis (H₀) and Alternative Hypothesis (H₁ or Ha)
b. Type I and Type II Errors - Type I Error (α) - Rejecting a true null hypothesis and Type II Error (β) - Failing to reject a false null hypothesis.
c. Significance Level (α)
d. P-value - The probability of obtaining the observed result, or more extreme, if the null hypothesis is true.
3. Steps in Hypothesis Testing with Examples :
a. Formulating Hypotheses - Setting up null and alternative hypotheses for different types of tests.
b. Selecting the Appropriate Test - Understanding which statistical test to apply based on the data and hypotheses
Z-test
t-test (one-sample, two-sample, paired t-test)
Chi-square test
ANOVA (Analysis of Variance)
d. Calculating the Test Statistic - Calculating Test Statistics using formulae.
e. Interpreting the results by comparing Test Statistic value with Significance Level and Drawing Conclusions
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