Key Points
- A null hypothesis is a way of testing for no relationship between data sets.
- A hypothesis is a way of testing for a relationship between data sets.
- You can use both tests in conjunction with one another during your analysis stage.
Null hypothesis vs. hypothesis, which is the right choice? When you get into the different methods of analyzing data, there is no shortage of tools at your disposal. Understanding the difference between a null hypothesis and a hypothesis can make or break your testing and analysis stages. Let’s dive into both of these tools and clarify which is best suited for a given application.
What Is the Null Hypothesis?
A null hypothesis is a prediction that there is no statistical relationship between two variables or two sets of data. Essentially, a null hypothesis assumes that any measured differences are the result of randomness and that the two possibilities are the same until proven otherwise.
The Benefits of a Null Hypothesis
A null hypothesis is commonly used in research to determine whether there is a real relationship between two measured phenomena. To this end, it offers the ability to distinguish between results that are the result of random chance or if there is a legitimate statistical relationship.
How to Create a Null Hypothesis
To create a null hypothesis, start by asking a few questions about the set of data or experiments. Then rephrase those questions into a statement that assumes no relationship. Subsequently, null hypotheses usually include phrases such as “no relationship,” “no effect,” etc.
For example, let’s say you are looking at some data about whether the number of people on a project affects the overall ability of the team to accomplish its goals.
A question might look like this:
“Does the number of people working on a team project impact the ability of the team to achieve the goals of the project?”
However, rephrasing this into a null hypothesis that assumes no relationship would look like this:
“The number of people working on a team project does not impact the ability of the team to achieve the goals of the project.”
The null hypothesis is assumed true until proven otherwise.
What Is a Hypothesis?
A hypothesis, also known as an alternative hypothesis, is an educated theory or “guess” based on limited evidence that requires further testing to be proven true or false. It is used in an experiment to define a relationship between two variables.
The Benefits of a Hypothesis
A hypothesis helps a researcher prove or disprove their theories, or guesses, using limited data and knowledge. In effect, researchers and scientists will create a formalized hypothesis based on past data or experiments. This hypothesis forces them to think about what they should be looking for in their experiments.
How to Create a Hypothesis
The best way to create a hypothesis is first to create a null hypothesis. Once you have your null hypothesis that states there is no relationship, you can then revise the statement that implies a relationship does exist. This is the reason it is referred to as an “alternative hypothesis.”
As an example:
Null hypothesis: There is no relationship between mediation and the reduction of depression.
Alternative hypothesis: The practice of meditation reduces depression.
In this example, the research wants to disprove that there is no relationship between meditation and the reduction of depression and prove that meditation does reduce depression. Specifically, the researcher’s goal is to prove their hypothesis through statistical data.
Null Hypothesis vs. Hypothesis: What’s the Difference?
In the simplest terms, a hypothesis is something that a researcher tries to prove, while a null hypothesis is something that a researcher tries to disprove. Both are used when performing research and evaluating data.
There are two variables in a hypothesis. The first is called the independent variable. This is the driving force of the experiment or research. The second is called the dependent variable, which is the measurable result.
However, the biggest difference between the two is that a null hypothesis cannot be proven; it can only be rejected.
Null Hypothesis vs. Hypothesis: Who Would Use Null Hypothesis and/or Hypothesis?
Having both a null hypothesis and hypothesis is beneficial and required in nearly all fields of research. Having both null and alternative hypotheses offers competing views in your research. Researchers weigh the evidence for and against the two hypotheses using a statistical test.
The statistical data is used to prove or disprove the alternative hypothesis. Additionally, If an alternative hypothesis is disproved, researchers can then modify their alternative hypothesis and look at their experimentation method(s) to achieve their goals and improve the accuracy of their experiments.
Why Does It Matter?
Doing any sort of hypothesis testing with your data serves a crucial purpose. By figuring out if effective and impactful changes have been made to your production, you’re getting closer to realizing the ideal of a 6 Sigma rating. It takes time and effort to conduct such tests, but if you’re looking to maximize quality while reducing waste, it pays to test.
Choosing Between Null Hypothesis and Hypothesis: Real World Scenarios
Null and alternative hypotheses are used extensively in medical research. As such, let’s say a team of researchers is trying to determine if flossing decreases the number of cavities a person might experience.
Their null hypothesis might look like this:
“There is no relationship between tooth flossing and the number of cavities a person experiences.”
Their alternative hypothesis might be:
“Tooth flossing reduces the number of cavities a person experiences.”
In the world of investing, a null hypothesis is frequently used in the quantitative analysis of data to test theories about economies, investing strategies, and other financial markets.
An example of a null hypothesis: The mean annual return of a stock option is 3%.
An example of an alternative hypothesis: The mean annual return of a stock option is NOT 3%.
Essentially, the theories are the alternative hypothesis you are trying to prove, and the null hypothesis is the statement you are trying to disprove.
Other Useful Tools and Concepts
While testing your data for meaningful changes is useful, there are other things to keep in mind when plotting your process improvement. Learning the differences between flowcharts and process maps can be vital when visualizing a given process. As such, you might want to learn the differences and benefits of these two tools.
Additionally, understanding the meaning behind repeatability and reproducibility can be crucial for your processes. These terms have general meanings used in everyday conversation, but understanding their definition within the context of your organization can lead to greater customer satisfaction.
Summary
The bottom line is that both types of hypotheses are required for proper research and data evaluation. Create a null hypothesis to disprove and an alternative hypothesis to prove. Collect and evaluate the data to determine which hypothesis is favored.
