Hypothesis Testing – A Complete Guide with Examples
Published byat August 14th, 2021 , Revised On August 25, 2023
What is a Hypothesis and a Hypothesis Testing?
A hypothesis is considered a belief or assumption that has to be accepted, rejected, proved, or disproved. In contrast, a research hypothesis is a research question for a researcher that has to be proved correct or incorrect through investigation.
Hypothesis testing is a scientific method used for making a decision, drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories to know whether or not the sample data support research. A research hypothesis is a predictive statement that has to be tested using scientific methods that join an independent variable to a dependent variable.
Characteristics of the Hypothesis to be Tested
A hypothesis should be:
- Clear and precise
- Capable of being tested
- Able to relate to a variable
- Stated in simple terms
- Consistent with known facts
- Limited in scope and specific
- Tested in a limited timeframe
- Capable of being tested
- Explain the facts in detail
What is a Null Hypothesis and Alternative Hypothesis?
A null hypothesis is a hypothesis when there is no significant relationship between the dependent and the participants’ independent variables.
In simple words, it’s a hypothesis that has been put forth but hasn’t been proved as yet. A researcher aims to disprove the theory. The abbreviation “Ho” is used to denote a null hypothesis.
If you want to compare two methods and assume that both methods are equally good, this assumption is considered the null hypothesis.
If you assume that one method is better than another method, then it’s considered an alternative hypothesis. The alternative hypothesis is the theory that a researcher seeks to prove and is typically denoted by H1 or HA.
If you support a null hypothesis, it means you’re not supporting the alternative hypothesis. Similarly, if you reject a null hypothesis, it means you are recommending the alternative hypothesis.
If a null hypothesis is rejected during the hypothesis test, even if it’s true, then it is considered as a type-I error. On the other hand, if you don’t dismiss a hypothesis, even if it’s false because you could not identify its falseness, it’s considered a type-II error.
How to Conduct Hypothesis Testing?
Step 1: State the Null and Alternative Hypothesis
Once you develop a research hypothesis, it’s important to state it is as a Null hypothesis (Ho) and an Alternative hypothesis (Ha) to test it statistically.
A null hypothesis is a preferred choice as it provides the opportunity to test the theory. In contrast, you can accept the alternative hypothesis when the null hypothesis has been rejected.
Step 2: Data Collection
Hypothesis testing follows the statistical method, and statistics are all about data. It’s challenging to gather complete information about a specific population you want to study. You need to gather the data obtained through a large number of samples from a specific population.
Step 3: Select Appropriate Statistical Test
There are many types of statistical tests, but we discuss the most two common types below, such as One-sided and two-sided tests.
Note: Your choice of the type of test depends on the purpose of your study
In the one-sided test, the values of rejecting a null hypothesis are located in one tail of the probability distribution. The set of values is less or higher than the critical value of the test. It is also called a one-tailed test of significance.
In the two-sided test, the values of rejecting a null hypothesis are located on both tails of the probability distribution. The set of values is less or higher than the first critical value of the test and higher than the second critical value test. It is also called a two-tailed test of significance.
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Step 4: Select the Level of Significance
When you reject a null hypothesis, even if it’s true during a statistical hypothesis, it is considered the significance level. It is the probability of a type one error. The significance should be as minimum as possible to avoid the type-I error, which is considered severe and should be avoided.
If the significance level is minimum, then it prevents the researchers from false claims.
The significance level is denoted by P, and it has given the value of 0.05 (P=0.05)
If the P-Value is less than 0.05, then the difference will be significant. If the P-value is higher than 0.05, then the difference is non-significant.
Step 5: Find out Whether the Null Hypothesis is Rejected or Supported
After conducting a statistical test, you should identify whether your null hypothesis is rejected or accepted based on the test results. It would help if you observed the P-value for this.
Step 6: Present the Outcomes of your Study
The final step is to present the outcomes of your study. You need to ensure whether you have met the objectives of your research or not.
In the discussion section and conclusion, you can present your findings by using supporting evidence and conclude whether your null hypothesis was rejected or supported.
In the result section, you can summarise your study’s outcomes, including the average difference and P-value of the two groups.
If we talk about the findings, our study your results will be as follows:
Did you know in your academic paper you should not mention whether you have accepted or rejected the null hypothesis?
Always remember that you either conclude to reject Ho in favor of Haor do not reject Ho. It would help if you never rejected Ha or even accept Ha.
Suppose your null hypothesis is rejected in the hypothesis testing. If you conclude reject Ho in favor of Haor do not reject Ho, then it doesn’t mean that the null hypothesis is true. It only means that there is a lack of evidence against Ho in favour of Ha. If your null hypothesis is not true, then the alternative hypothesis is likely to be true.
Frequently Asked Questions
The 3 types of hypothesis tests are:
- One-Sample Test: Compare sample data to a known population value.
- Two-Sample Test: Compare means between two sample groups.
- ANOVA: Analyze variance among multiple groups to determine significant differences.