{"id":903,"date":"2019-04-23T12:08:35","date_gmt":"2019-04-23T10:08:35","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/?p=903"},"modified":"2026-07-13T00:29:53","modified_gmt":"2026-07-12T22:29:53","slug":"statistics-tutorial-3-hypothesis-testing","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/statistics-tutorial-3-hypothesis-testing\/","title":{"rendered":"Statistics Tutorial 3 \u2013 Hypothesis Testing"},"content":{"rendered":"

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Updated July 12, 2026:<\/strong> Updated links and SEO. See also 20 hypothesis testing solved examples<\/a> and hypothesis testing basics<\/a>.<\/p>\n<\/div>\n

In this tutorial we are going to cover Hypothesis Testing. To understand this topic better, we would break it down into the following sub-topics<\/p>\n

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  1. About Estimation<\/a><\/li>\n
  2. Introduction to Hypothesis Testing<\/a><\/li>\n
  3. Confidence Interval<\/a><\/li>\n
  4. t-Statistic<\/a><\/li>\n
  5. p-Value<\/a><\/li>\n<\/ol>\n

     <\/p>\n

    1. About Estimation<\/strong><\/h4>\n

    Estimation is a statistical way of trying to deduce the value of an unknown parameter. For example, to estimate the mean of a population \u00b5, we can take a sample from the population and calculate the mean. Then we can use the sample mean as an estimate of the population mean.<\/p>\n

     <\/p>\n

    Point Estimate vs Interval Estimate<\/strong><\/p>\n

    A point estimate<\/em> is one single number that is represents the parameter you are trying to estimate.<\/p>\n

    Interval estimates<\/em> is a range of values that represents the parameter you are trying to estimate. Hence, interval estimate are often two values that define a range.<\/p>\n

    The question now is: how accurate is our estimate? We can get this by performing hypothesis testing.<\/p>\n

     <\/p>\n

    2. Introduction to Hypothesis Testing<\/strong><\/h4>\n

    Hypothesis testing is simply a statistical way of testing an existing\u00a0 or null hypothesis H0<\/sub>(that is an estimate the is currently accepted). Therefore, to carry out a hypothesis, there must at least be an existing hypothesis. So we have to test the null hypothesis to see if it is correct.<\/p>\n

    To do this we need to formulate an alternative hypothesis Ha or H1<\/sub>. This is normally exactly opposite of the null hypothesis.<\/p>\n

    Let’s take example of regression from Machine Learning 101<\/a>. We make an estimate of the regression coefficient\u00a0\u03b21<\/sub> in case of linear regression.<\/p>\n

    Let’s state the null and alternate hypothesis:<\/p>\n