This document discusses the fundamentals of hypothesis testing. It introduces important terms like population, sample, statistical inference, parameters, and statistics. It explains that hypothesis testing is a form of statistical inference used to generalize from a sample to a population. The document outlines the steps for hypothesis testing, including formulating the null and alternative hypotheses, selecting an appropriate test, determining critical values, and deciding whether to reject or not reject the null hypothesis. It provides examples and discusses type I and type II errors.

1. FUNDAMENTALS OF
HYPOTHESIS TESTING
- BY MONIDEEPA MULLICK
M.COM(ACCOUNTING & FINANCE), PGDM(BUSINESS ANALYTICS)

2. Introducing with some important terms
• Population  all possible values
• Sample  a portion of the population
• Statistical inference  generalizing from a sample to
a population with calculated degree of certainty
• Two forms of statistical inference
– Hypothesis testing
– Estimation
• Parameter  a characteristic of population, e.g., population
mean µ
• Statistic  calculated from data in the sample, e.g., sample
mean ( )x

3. WHY DO WE
NEED
HYPOTHESIS ?

4. DEFINITION

5. Steps for Hypothesis Testing
Draw Marketing Research Conclusion
Formulate H0 and H1
Select Appropriate Test
Choose Level of Significance
Determine Prob
Assoc with Test Stat
Determine Critical
Value of Test Stat
TSCR
Determine if TSCR
falls into (Non)
Rejection Region
Compare with Level
of Significance, 
Reject/Do not Reject H0
Calculate Test Statistic TSCAL

7. EXAMPLE: 1

8. EXAMPLE: 2

9. EXAMPLE: 3

10. TYPE I AND TYPE II ERROR

11. LETS UNDERSTAND WITH AN
INTERESTING EXAMPLE
TYPE I AND TYPE II ERROR

12. CONCLUSION
LETS SEE HOW ARE HYPOTHESIS DRAWN IN OTHER
STATISTICAL TOOLS