Hypothesis Testing

Hypothesis Testing

Hypothesis testing is a method of statistical inference. It is used to test if a statement regarding a population parameter is statistically significant. For testing the power of predictions hypothesis can be used as a powerful tool. For example, if a financial analyst wants to make predictions regarding the mean value a customer would pay for the firm’s product so she can formulate a hypothesis where the average value that customers will pay for the product is greater than $7. To statistically test this the firm owner can use the hypothesis. Hypothesis testing is a critical part of the scientific method which is a systematic approach to assessing theories through observation. For an analyst who makes a prediction hypothesis testing is a rigorous way of backing up his prediction with statistical analysis.

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How does it work?

An analyst tests a statistical sample with the goal of providing evidence on the plausibility of the null hypothesis. A hypothesis is tested by a statistical analyst by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses i.e., the null hypothesis and alternate hypothesis. The null hypothesis is usually a hypothesis of equality between population parameters. For example, a null hypothesis may state that the population mean return is equal to zero. The alternate hypothesis is the opposite of the null hypothesis which means it may state that the population mean is not equal to zero. Thus, they are mutually exclusive and only one will be true. However, one of the two hypotheses will always be true.

Steps involved in Hypothesis Testing


Ha: µ0≠0


  • H0 = null hypothesis
  • Ha = alternate hypothesis

We also need to calculate the test statistic to reject the null hypothesis for which the formula is below-

These are the steps to be followed for hypothesis testing –

  • The null hypothesis(H0) and the alternative hypothesis(Ha) must be stated.
  • The statistical assumptions made need to be considered and evaluate if these assumptions are coherent with the underlying population being evaluated.
  • The appropriate probability distribution needs to be determined and the appropriate test statistic needs to be selected.
  • The significance level has to be selected which is denoted by the Greek letter alpha (α). This is the probability threshold for which the null hypothesis will be rejected.
  • Based on the significance level and the appropriate test statistic the decision rule.
  • The test statistic can be calculated using the observed sample data.
  • Based on the test statistic result the hypothesis should be rejected or fail to reject which is known as the statistical decision.
  • There are some non-statistical considerations that need to be taken in order to reach the final decision.

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If we want to see the degree of relation between two stock prices and the significance value of the correlation coefficient is greater than the predetermined significance level then we can accept the null hypothesis and conclude that there was no relation between the two stock prices. However, the chance factor shows a relation between the variables.

Hypothesis testing is helpful while developing the business. If in the business they train the outside sales force and want to know whether a specific sales technique results in a higher close ratio than the methods currently employed by the company. The null hypothesis will be the new technique has no effect on the sales which isn’t explained by random chance, while the alternative hypothesis will be that the method does not have any effect either positive or negative. if the conclusion received is that the technique has an effect and it is positive then the company can implement the new method and it would get results. Hypothesis testing is a simple process if broken down into steps.

Hypothesis testing allows a mathematical model to validate a claim or idea with a certain confidence level but like other model has its own limitations. When this model is used to make financial decisions, a critical eye should be kept keeping all dependencies in mind.

Author -Sanjana Rau

About the author- Started my journey of self even when the odds were against me, keen observation, a cool temper, and sports worked the best for me.

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