• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Understanding Hypothesis Testing and p-value

Quantitative Finance, Statistics

Behavioral scientists, market researchers, astrophysicists, drug testers all seek to better understand the target group. Often it is next to impossible to assess the entire population. Inferential statistical testing is instead done on a sample that exhibits most if not all characteristics of the population. This is done using hypotheses testing.

Hypothesis (plural form being hypotheses) refers to a supposition which serves as the starting point for further exploration. Hypothesis testing states a ‘status quo’ hypothesis also known as the null hypothesis. The hypothesis that is the opposite or proposes another alternative is called the alternative hypothesis.

In hypotheses testing we start by assuming that the null hypothesis is in fact true.  We then try to find what is the probability that the null hypothesis is true. If the probability turns out to be very small then we can say that the null hypothesis is not true.

If, for example, a brand of beer wants to test if working men consume 3 beers or more on an average during a Saturday, in order to place more ads on Friday. It will first have to clearly state its claim or null hypothesis. Next a random sample from the population is collected. This could be, for example, 30 working men and the number of beers they consumed on a given Saturday. The mean of the same is calculated.

The sample mean is then compared to the supposition we have made. If it is found that the difference between the sample mean and population mean is too small then we accept the null hypothesis, which is that working men drink three or more beers on a Saturday. If the difference is large between the two we reject the null hypothesis.

The probability value that we get that helps us accept or reject the null hypotheses is called the p-value. A p-value less than 5% usually means the null hypothesis is to be rejected. In this context we refer to significance. A null hypothesis is rejected, since the p-value is less that 5%, we say significance has been reached. Alternatively when the p-value is more than 5%, the null hypotheses is retained and we say significance has not been reached. The result is not significant enough for it to be stated. A third alternative is that the p-value is at 5%. This means the hypothesis can go either which way. Clearly no conclusion can be derived from this.

In our case if the p-value was more than 5%, the beer brand will go ahead with more advertising on Fridays. If not they will continue with their current advertising plan. We will look at how to calculate p-value in another article.

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us