In the world of data-driven business, academia, and quality improvement, the role of hypothesis plays a critical role. Without a hypothesis, decisions are made based on inaccurate information and can lead to unintended consequences. If you’re interested in improving your decision-making process with statistical analysis, this tutorial will teach you the basic steps of hypothesis testing and null hypothesis.
The first step in hypothesis testing is to formulate a hypothesis. This hypothesis is your initial view of the process that produced your data. You can then restate your hypothesis as either the null hypothesis or the alternate hypothesis. The null hypothesis reveals that there is no relationship between the two variables, whereas the alternate hypothesis states that there is.
To test your hypothesis, you can use a test statistic. Usually, a test statistic is used to compare two variables or groups. For example, if you have 20 students, you’ll need to calculate the p-value for each one. The p-value is a measure of the probability that a single sample statistic is extreme by chance. Depending on the significance level, you can use this p-value to come to conclusions about your hypothesis.
Using statistics to test a hypothesis is useful in many situations. For example, a drug may effectively treat headaches, but if the results cannot be replicated, the experiment will have no effect. In contrast, a discovery like cold fusion might fade into obscurity because no one could duplicate the results. Statistical analysis is also useful in solving homework problems, finding new species, and improving standardized tests.
What is Hypothesis Testing?
Hypothesis Testing means analyzing an assumption about the population framework. Hypothesis testing is to make a hypothesis about the population using statistics. It is a statistical interference and uses sample data to make conclusions about population parameters or a population popularity distribution. There are mainly two types of hypothesis testing null hypothesis and alternative hypothesis. An alternative hypothesis is the opposite of the null hypothesis.
Firstly a tentative hypothesis is made regarding the parameter or distribution. The first hypothesis is known as the null hypothesis. Analysts’ methods depend on the nature of data and the reason for the analysis, like why it is done.
What is Null Hypothesis?
In statistics, the null hypothesis states that there is no connection between two population parameters, meaning that there is no connection between the independent and dependent variables. If the result of the hypothesis shows that there is connected between two parameters, then the result is portrayed this way due to sampling error. This means that the null hypothesis is false by establishing a connection between the parameters.
Now let’s understand how the null hypothesis works.
A null hypothesis statement is a plant does not get affected by oxygen. Could prove this statement by keeping the plant in and without air.
- The null hypothesis is a complete hypothesis that needs to be proved. Only then could the statement be proved true or false.
- The null hypothesis leaves the scope of doing further experiments on the parameters.
- If we deny or reject the null hypothesis, it does not mean we didn’t get the expected results.
- Denying a null hypothesis further expands the scope of finding the relationship between two variables.
Hypothesis Testing example in Real life
co could determine hypothesis testing examples by firstly understanding null and alternative hypotheses. While hypothesis testing there are specific criteria of that which are mentioned below –
How to test a hypothesis –
- Explain a null hypothesis
- Explain an alternative hypothesis
- Regulate a significant level – a significant level is set at 0.5 and could also go to 0.1 or 0.01. It helps in defining the probability of rejection in the null hypothesis.
- Find the p-value – the p-value is for getting the results for the null hypothesis.
- Conclude – if the p-value is 0.1 and the significant value is 0.05, then the null hypothesis is rejected, and the alternative hypothesis is taken up.
Hypothesis testing example is –
Vitamin C
It is said that vitamin C heals the common cold, and let’s make a hypothesis on the same.
- Null hypothesis – children who take vitamin c are likely to catch a common cold.
- The alternative hypothesis – children who take vitamin c do not catch a common cold.
- Significance level – 0.05
- P value – 0.20
- Conclusion – testing the hypothesis of whether consuming vitamin c prevents the common cold. After the analysis, the conclusion p value turns out to be 0.20. the desired significant value was 0.05. Thus null hypothesis is not rejected, and the alternative view has been denied. It means that vitamin c cannot prevent the common cold.
The null hypothesis example
It differentiates the null hypothesis from another one, written as Ho.
A hypothesis test is done to determine whether the null hypothesis is true or false. Here is a null hypothesis example-
There is a company XYZ, whose annual return is 7.5%. The statement is that XYZ’s annual return is not 7.5%. For hypothesis testing, we will first accept the null hypothesis. The information opposite the null hypothesis is taken as the alternative hypothesis. The alternative hypothesis statement is that the XYZ company’s annual return is 7.5%. The sample of five years is considered, and the result is compared to the assumed yearly return of 7.5%. The annual return of XYZ company is 7.5%, and the null hypothesis is rejected.
Steps in Hypothesis Testing
The basic idea of hypothesis testing is following a procedure. The analysts use this formal procedure as a hypothesis. There are steps in hypothesis testing which are followed during the procedure, which are as follows –
Step 1 – Statement of Null and Alternate hypothesis
After deciding on the initial hypothesis, choose the statement of null and alternate hypotheses (Ho) and (Ha). These statements help in the procedure and research. These statements help determine whether w would reject the null hypothesis or not and whether the hypothesis is in favor of the alternative hypothesis.
Step 2 – Collection of data
Collecting data and samples is essential for the abscess of their hypothesis. The data should be representative of the data because it could not perform the hypothesis.
Step 3 – Perform a statistical test.
Various statistical tests could be performed—for example, within the group varies, and between the group variances. If the group variance is enormous, then the p-value would be low. Instead, the p-value is high within the group if variances are high.
Step 4 – null hypothesis is rejected or in favor.
The p-value decides whether the decision will reject the null hypothesis, and there is less chance that the null hypothesis is in favor.
Step 5 – Present the Hypothesis
Make a section in the researcher’s paper presenting the finding of the hypothesis. Make many departments such as – data and samples, summary, methodologies, etc. make a summary of the discovery. Mention all the detain from the initial stage to the ending stage. Also mentioned, the null hypothesis that was the result in favor of the null hypothesis was rejected.
Hypothesis Testing Methods
We have learned about various aspects of hypothesis testing. The null hypothesis is rejected when the p-value is less than the significant level. There are mainly two types of hypothesis testing methods –
Frequentist hypothesis testing
The frequentist or traditional approach is a type of method which considers current data for making assumptions. A frequent popular kind of hypothesis testing is null hypothesis significant testing ( NHST), the premise of truth and present data, and a set of two hypotheses are formulated. NHST is one of the most popular types of hypothesis used since the mid-1950s.
Bayesian hypothesis testing
Bayesian hypothesis testing is the modern type of testing. It is the opposite of frequentist hypothesis testing. The methodology uses past data or samples to generate hypothesis testing results. The past samples are known as the prior probability. The prior probability is sure to find the current and logical hypothesis. The final result represents a reasonable probability of the hypothesis. The researcher relies on the prior and plausible probability for the hypothesis testing.
Based on the prior probability, whether the Bayesian hypothesis testing tests a hypothesis to be true or false is found. The main component of this method is the Bayes factor which demonstrates the null and alternate hypotheses, and the Bayes factor helps indicate the plausibility of the two hypotheses.
Conclusion
After reading this article, you will learn about hypothesis testing. The topic is covered in broad scope, including the example, methods, and null hypothesis. The hypothesis is used for making guesses about the assumption using statistics. Different types of data and methodology are sued in this process, and one of the primary roles is played by null and alternate hypotheses.
