Know It All About Hypothesis Testing: A Simplified Guide
Table of Contents
- jaro education
- 13, August 2024
- 3:00 pm
Overview
Hypothesis testing is one of the major methods in the grand scheme of statistics. It assists in deriving data-driven decisions, evaluating theories, and inferring meaningful conclusions from data. But what is hypothesis testing, and why is it so important in statistics? For those who want to learn and enhance their skills, the IIM Nagpur PG Certificate Programme in Data Science for Business Excellence and Innovation offers the perfect platform for learning from professionals, gaining hands-on experience, and moving up the value chain.
Introduction to Hypothesis Testing
Hypothesis testing is a means to make decisions about population properties based on the information derived from a sample. It simply provides a basis for the testing of assumptions or hypotheses made about a certain parameter in a population. Statement of the whole process involves a null hypothesis denoted by H0 and an alternative hypothesis denoted by H1, followed by the collection of data and subsequent analysis to see whether it favours the rejection of the null hypothesis in favour of the alternative.
In other words, hypothesis testing gives the answers to such questions as:
- Does a new drug improve patient outcomes?
- Is a new teaching method more effective than the old one?
- Does a marketing campaign increase sales?
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The Hypothesis Testing Process
The hypothesis testing process is quite logical and it contains several critical steps:
- State the Hypotheses: The hypotheses state the null hypothesis, H0, and the alternative hypothesis, H1. The null hypothesis normally represents a no-effect or no-difference statement, while the alternative represents what is to be proved.
- Select the Level of Significance (α): Probability of rejecting the null hypothesis when it is true. Common values are 0.05, 0.01, and 0.10.
- Choose the Right Test: Depending on the data and hypothesis, the test to be applied will be different, it could be anything from a t-test and chi-square test, among others.
- Gather and Summarise the Data: Get the data either through experiments or employing observation and then summarize the same through relevant statistics.
- Calculate the Test Statistic: The test statistic is calculated with the help of data. It expresses the probability that the sample data will be observed under the null hypothesis.
- Make a Decision: Compare the computed value of the test statistic to the critical value obtained from the statistical table or, using the p-value, accept or reject the null hypothesis.
- Draw a Conclusion: Using the results in the s