Having a strong hypothesis is not enough to conduct research. The testability in technical hypothesis also plays a major role. Besides having a researchable hypothesis, it is imperative for the hypothesis to be testable too. This is what testability means in reality. It refers to the ability to run a test or experiment to approve or disapprove of the proposition made. Testability in technical hypothesis accounts for the research methods and constraints that hinder the testing process.
Many research students try to formulate a proposition that is strong. What do they forget about the technical hypothesis? Yes, it is the testability of that hypothesis. Therefore, today’s article is all about explaining the testing of a hypothesis and its importance in research. However, before going into that discussion, let’s define a testable hypothesis.
What Does It Mean By Testable Hypothesis?
A testable hypothesis is the suggested proposition that can be investigated to see if it is true or false. To define a testable hypothesis, researchers often the use term testability. Testability is about running a test or an experiment on a technical hypothesis to approve or disapprove it. For example, the hypothesis in research is, “The moon is made of green cheese.” This hypothesis is a testable one. It does not matter whether this proposition is true or false. The testability of the hypothesis is what matters. The moon hypothesis is testable because the researcher can go to the moon, collect some samples, and test them in the laboratory.
Why Is Testability In Technical Hypothesis Important?
From the discussion above, you know the meaning of testability in technical hypothesis. Knowing this is not enough as you must also know its importance. So, according to the San Jose State University Statistics Department, USA, hypothesis testability is one of the most important concepts in research. A brief description of the points that make this testing important is as follows as shared by British dissertation help providers:
- It is the testability that tells a researcher whether the designed hypothesis is researchable or not. It tells whether it is possible to prove or disapprove of the null hypothesis using the current technologies or testing methods or not.
- Testing a hypothesis tells whether the thing the researcher is going to investigate really happened or not.
- It tells the effects of certain treatments on hypotheses. It means that testability is important in describing whether certain treatments on the hypothesis are going to have a positive or negative effect on the hypothesis.
- By testing a hypothesis, the researcher knows if certain groups differ from each other. If they differ, the testability of the hypothesis also helps him understand the extent of that difference.
- Testability is also important to prove that your hypothesis is statistically significant and is unlikely to have occurred by chance. I repeat again; it is the outcome that falsifies or approves a hypothesis.
How To Perform Testing Of A Hypothesis?
After learning the importance and definition of testability in technical hypothesis above, it is time to shed some light on the process of testing a hypothesis. Hypothesis testing is about applying the formal procedure to approve or disapprove the proposition. Hence, a brief description of the steps involved is as follows:
State Your Null And Alternate Hypothesis
Stating the null hypothesis is the first step in the process of testing a hypothesis. The null hypothesis is a prediction of no relationship between the variables. On the other hand, the alternate hypothesis must represent some kind of relation between the variables. Represent the null hypothesis with Ho and the alternate hypothesis with Ha. For example:
Ha = Men are taller than women, on average.
Ho = Men are not taller than women, on average.
Collect The Data Regarding Your Hypothesis
The second step is the collection of data. For a technical hypothesis to be valid, you need to perform some kind of sampling. So, collect the data in a way that can be helpful in testing the designed hypothesis. Note that all of your data should be representative of the hypothesis. Failure to collect such data does not lead you to prove or disapprove of your hypothesis.
Perform A Test On The Data
After the data collection, the next step is the application of a statistical test on the dataset. There are a variety of tests available to use for testability in technical hypothesis. You can use a t-test, z-test, or ANOVA test based on the problem. Hence, choose a test best suited for your data and research objectives. For example, to test the hypothesis mentioned above, the t-test is the best method. However, if you are facing any issues in performing the test, get dissertation help online.
Decide Whether To Approve Or Disapprove Of The Hypothesis
Based on the outcome of the statistical test, you have to take one side of your hypothesis. Whether you are going to approve that test or disapprove, it depends on the results of the test. Most of the time, you use the p-value generated within the test to decide this. If the p-value is less than 5%, it means that your null hypothesis is true. Anything higher than this points towards the falsification of the hypothesis.
Present Your Findings
The next step is to present your findings in a documented form. The results of the test, whether positive or negative, are reported in the results section of the research paper, thesis, or dissertation. In the results section, give a summary of the notable results found after testing the hypothesis. The results of the test are actually representing the testability of the hypothesis. So, you can either reject or accept the hypothesis based on the findings.
Conclusion
Testability in the technical hypothesis is about checking whether a proposition is testable or not. It also includes checking its testability against the current statistical methods or tests. It is important to check the testability because it tells you about the actual happenings and the relationship between the variables. So, read the steps mentioned above to have an idea of how to perform the testing of a hypothesis.