# Hypothesis Testing: A Step-by-Step Guide With Easy Examples

**Introduction**

When we hear the word ‘hypothesis,’ 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 **hypothesis testing** has grabbed everyone’s attention.

Let us find out more about **testing of hypothesis** and the different steps through which you can write a hypothesis.

**What is Hypothesis?**

A hypothesis’s general definition says, “Hypothesis is an assumption made based on some evidence.” 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.

**Key Points of a Hypothesis**

- The assumptions made while proposing the theory should be precise and based on proper evidence.
- The hypothesis should target a specific topic only and should have the scope to conduct various experiments for proving the assumptions.
- 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.

**Types of Hypotheses With Examples**

There are multiple types of hypotheses which are described below.

**1. Simple Hypothesis**

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.

**Example:
**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.

**2. Complex Hypothesis**

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.

**Example:**

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.

**3. Null Hypothesis**

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.

**Example:**

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.

**4. Alternative Hypothesis**

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.

It is to be noted that H0 ≠ H A. The alternate hypothesis further branches into two categories:

**Directional Hypothesis:**The result obtained through this type of alternative hypothesis is either negative or positive. It is represented by adding ‘>’ or ‘<‘ along with the HA symbol.

**Non-Directional Hypothesis:**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.

**Example:**

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.

- 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
- If you only prove that the chances of heart disease depend on variables like age and daily routine, it is a non-directional hypothesis.

**5. Logical Hypothesis**

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.

**Example:**

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.

**6. Empirical Hypothesis**

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.

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.

**Example:**

Vegetables grow faster in cold climates as compared to warm and humid climates. The assumption stated here can be thoroughly tested through scientific methods.

**7. Statistical Hypothesis**

Statistical Hypothesis makes use of large statistical datasets to obtain results that consider larger populations. 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.

**Example:**

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.

**What Makes a Good Hypothesis?**

Before developing a good hypothesis, you must consider a few points.

- Do the assumptions made in the hypothesis consist of dependent or independent variables?
- Can you conduct safety tests for your assumptions in the hypothesis?
- Are there any other alternative assumptions present that you can take into consideration?

Characteristics of a Good Hypothesis –

**1. Candid Language**

Make use of simple language in your hypothesis instead of being vague. Try to focus on the given topic through your assumptions; it should be simple yet justifiable. The use of candid language makes the hypothesis more understandable and reachable to the common people.

**2. Cause and Effect**

Understand the assumptions made in the hypothesis. For example, the cause of the assumption, the effect of the assumption being accepted or rejected, etc. Try to back up your assumptions with the help of proper scientific data and explanations.

**3. The Independent and Dependent Variables**

Before starting to write a hypothesis, figure out the number of dependent and independent variables in the hypothesis. This will help you make proper assumptions to establish a link between these variables or to prove that these variables are not interlinked. It will also help you to prepare a mind map for your hypothesis.

**4. Accurate Results**

One of the most important characteristics of a good hypothesis is the accuracy of the results. Hypotheses are generally used to predict the future based on current scenarios. This can help to figure out the problems that may arise in the future and find solutions accordingly.

**5. Adherence to Ethics**

Sticking to ethics while working on any research project is very important. You get an idea about the research structure through the generally followed ethics beforehand. It helps to guide the research project or hypothesis in a fruitful direction.

**6. Testable Predictions**

The conditions used in the hypothesis research project should be easily testable. This helps to make the results of the hypothesis more accurate and reliable. Before starting the research on the assumptions in the hypothesis, you should be aware of all the different ways that can be used to make the hypothesis applicable to modern testing methodologies.

**How to Write a Hypothesis?**

Well, there are many ways to write a hypothesis; here are the six most efficient and important steps that will help you craft a strong hypothesis:

**Step 1: Ask a Question**

The first and most important step of writing a hypothesis is deciding upon the questions or assumptions you will implement in your research. A hypothesis can’t be based on random questions or general thoughts. The questions you decide must be approachable and testable as it forms the foundation of your project.

**Step 2: Carry out Preliminary Research**

Once you have decided on the questions and assumptions to be included in your hypothesis, you should start your preliminary research on the same. For that, you should start reading older research papers on the topic, go through the web, collect the data, prepare the dataset for the experiments, etc.

**Step 3: Define Your Variables**

After conducting the preliminary research, you need to define the number of variables present in your assumption and classify them into dependent and independent variables. It will help you to conduct further research and establish a link between them or prove that there is no link between them.

**Step-4: Collect Data to Support Your Hypothesis**

After classifying the variables and conducting the basic preliminary research, you need to start collecting evidence and data that will help you support your hypothesis. This data will help you test your assumptions and infer statistical results about your interesting dataset.

**Step-5: Perform Statistical Tests**

The data you have collected from the above step can be used to perform different statistical tests. The type of tests you perform depends on the data you collect. All the different tests are based on in-group variance and between-group variance. Depending on the variance, your statistical test will reflect a high or low p-value.

After performing the tests, you should prepare a draft for writing down your hypothesis.

**Step-6: Present It in an If-Then Form**

Now that everything has been done, it is time to write down your hypothesis. Considering your draft, you should write down the hypothesis accordingly and ensure that it satisfies all the conditions like simple and to-the-point language, accurate results, relevant evidence and data sources, etc. The final hypothesis should be well-framed and address the topic clearly.

**Conclusion**

Research and **hypothesis testing** are an important part of the Business Analytics field. To write a good hypothesis or research, you need to conduct a good amount of research. Since you know about the different types of hypotheses and how to write a good hypothesis, writing a good and strong hypothesis by yourself is now much easier! If you want to pursue a career in the field of Business Analytics, you can check out the Integrated Program In Business Analytics by UNext Jigsaw. We hope now you understand “**what is hypothesis testing**?” and **hypothesis testing steps** in detail.