{"id":256550,"date":"2022-10-21T07:47:51","date_gmt":"2022-10-21T02:17:51","guid":{"rendered":"https:\/\/www.jigsawacademy.com\/?p=256550"},"modified":"2022-10-21T07:49:14","modified_gmt":"2022-10-21T02:19:14","slug":"hypothesis-testing-a-step-by-step-guide-with-easy-examples","status":"publish","type":"post","link":"https:\/\/www.jigsawacademy.com\/blogs\/business-analytics\/hypothesis-testing-a-step-by-step-guide-with-easy-examples\/","title":{"rendered":"Hypothesis Testing: A Step-by-Step Guide With Easy Examples"},"content":{"rendered":"

Introduction<\/span><\/b>\u00a0<\/span><\/h3>\n

When we hear the word \u2018hypothesis,\u2019 the first thing that comes to our mind is a kind of theory. Assuming and explaining theories is a fundamental part of Business Analytics. In the past few years, the field of Business Analytics has proliferated and made several advancements. As the number of people interested in its statistical applications in business has increased, the concept of <\/span>hypothesis testing<\/span><\/b> has grabbed everyone\u2019s attention.<\/span><\/p>\n

Let us find out more about <\/span>testing of hypothesis<\/span><\/b> and the different steps through which you can write a hypothesis.<\/span>\u00a0<\/span><\/p>\n

What is Hypothesis?<\/span><\/b>\u00a0<\/span><\/h2>\n

A hypothesis’s general definition says, \u201cHypothesis is an assumption made based on some evidence.\u201d It is a theory you propose about what will happen in the future based on current circumstances. Proposing a hypothesis is the first and most important step of any research or investigation as it decides the future path of the research\/investigation and can lead it to a faithful and acceptable answer.<\/span>\u00a0<\/span><\/p>\n

Key Points of a Hypothesis<\/span><\/b>\u00a0<\/span><\/h2>\n
    \n
  • The assumptions made while proposing the theory should be precise and based on proper evidence.<\/span>\u00a0<\/span><\/li>\n
  • The hypothesis should target a specific topic only and should have the scope to conduct various experiments for proving the assumptions.<\/span>\u00a0<\/span><\/li>\n
  • The sources used for developing a hypothesis must be based on scientific theories, common patterns that affect the thought process of the people, and observations made in past research programs on the same topic.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

    Types of Hypotheses With Examples<\/span><\/b>\u00a0<\/span><\/h2>\n

    There are multiple types of hypotheses which are described below.<\/span>\u00a0<\/span><\/p>\n

    1. Simple Hypothesis<\/span><\/b><\/p>\n

    As the name suggests, a simple hypothesis is pretty simple to work on. It just deals with a single independent variable and one dependent variable. While proving a simple hypothesis, you just have to confirm that these two variables are linked.<\/span>\u00a0<\/span><\/p>\n

    Example:
    \n<\/span><\/b>If you eat more vegetables, you will be safe from heart disease. Here eating vegetables is an independent variable and staying safe from heart disease is a dependent variable.<\/span>\u00a0<\/span><\/p>\n

    2. Complex Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

    Unlike a simple hypothesis, a complex hypothesis deals with multiple dependent and independent variables in the assumption simultaneously. The involvement of multiple variables makes the hypothesis more accurate and more difficult to prove simultaneously.<\/span>\u00a0<\/span><\/p>\n

    Example:<\/span><\/b>
    \n<\/span>Age, diet, and weight affect the chances of diseases like diabetes or blood pressure. Age, diet, and weight are independent variables, and diabetes and blood pressure are dependent variables.<\/span>\u00a0<\/span><\/p>\n

    3. Null Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

    The null hypothesis is the opposite of the simple hypothesis. Where a simple hypothesis tries to establish a link between the dependent and the independent variables, the Null hypothesis tries to prove that there’s no link between the given variables. Simply put, it tries to prove a statement opposite to the proposed hypothesis. It is represented as H0.<\/span>\u00a0<\/span><\/p>\n

    Example:<\/span><\/b>
    \n<\/span>Age and daily routine affect the chances of heart disease. In a Null hypothesis, you will try to prove that there is no relation between the given factors, i.e., age, weight, and heart disease.<\/span>\u00a0<\/span><\/p>\n

    4. Alternative Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

    An alternative hypothesis tries to disapprove the assumptions or statements proposed in a null hypothesis. Generally, alternative and null hypotheses are used together. An alternative hypothesis is represented as HA.<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span>It is to be noted that H0 \u2260 H A.<\/span>\u00a0<\/span>The alternate hypothesis further branches into two categories:<\/span>\u00a0<\/span><\/p>\n

      \n
    • Directional Hypothesis:
      \n<\/span><\/b>The result obtained through this type of alternative hypothesis is either negative or positive. It is represented by adding \u2018>\u2019 or ‘<‘ along with the HA symbol.<\/span><\/li>\n
    • Non-Directional Hypothesis:
      \n<\/span><\/b>This type of hypothesis only clarifies the dependency of the dependent variables on the independent variable. It does not state anything about the result being positive or negative.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

      \u00a0<\/span>Example:<\/span><\/b>\u00a0<\/span><\/p>\n

      Age and daily routine affect the chances of heart disease. In an Alternative Hypothesis, you will try to prove that age and daily routine affect heart disease chances.<\/span>\u00a0<\/span><\/p>\n

        \n
      • If you prove the result is positive or negative, i.e., age and daily routine do or do not affect the chances of heart disease, it is a directional hypothesis<\/span>\u00a0<\/span><\/li>\n
      • If you only prove that the chances of heart disease depend on variables like age and daily routine, it is a non-directional hypothesis.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

        5. Logical Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

        Logical hypotheses cannot be proved with the help of scientific evidence. The assumptions made in a logical hypothesis are based on some logical explanation that backs up our assumptions. Logical hypotheses are mostly used in philosophy, and as the assumptions made are often too complex or simply unrealistic, they are untestable, and we have to rely on logical explanations.<\/span>\u00a0<\/span><\/p>\n

        Example:<\/span><\/b>\u00a0<\/span><\/p>\n

        Dinosaurs are related to the reptile family as both have scales. As the dinosaurs are extinct, we cannot test the given hypothesis and rely on our logical explanation on, not the experimental data.<\/span>\u00a0<\/span><\/p>\n

        6. Empirical Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

        It is the complete opposite of the Logical Hypothesis. The assumptions made in an Empirical Hypothesis are based on empirical data and proved through scientific testing and analysis.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

        It is divided into two parts, namely theoretical and empirical. Both methods of research rely on testing that can be verified through experimental data. So, unlike logical hypotheses, an empirical hypothesis can be and will be tested.<\/span>\u00a0<\/span><\/p>\n

        Example:<\/span><\/b>\u00a0<\/span><\/p>\n

        Vegetables grow faster in cold climates as compared to warm and humid climates. The assumption stated here can be thoroughly tested through scientific methods.<\/span>\u00a0<\/span><\/p>\n

        7. Statistical Hypothesis<\/span><\/b>\u00a0<\/span><\/p>\n

        Statistical Hypothesis makes use of large statistical datasets to obtain results that consider larger populations.\u00a0 This type of hypothesis is used when we have to take into consideration all the possible cases present in the assumptions made in the hypothesis. It makes use of datasets or samples so that conclusions can be drawn from the broader dataset. For this, you may conduct tests for sufficient samples and obtain results with high accuracy that would remain stable across all the datasets.<\/span>\u00a0<\/span><\/p>\n

        Example:<\/span><\/b>\u00a0<\/span><\/p>\n

        Men in the U.S.A. are taller than men in India. It is simply impossible to measure the height of all the men present in India and the U.S.A., but by conducting the test on sufficient samples, you can obtain results with high accuracy that would remain constant over different samples.<\/span>\u00a0<\/span><\/p>\n

        What Makes a Good Hypothesis?<\/span><\/b>\u00a0<\/span><\/p>\n

        Before developing a good hypothesis, you must consider a few points.<\/span>\u00a0<\/span><\/p>\n