In the age of big data, both businesses and individuals rely on data to make meaningful decisions. Hypothesis testing is a core skill to have for all data scientists and even most business analysts. In hypothesis testing, we can make inferences about populations from sample data based on statistics, which is why it forms an important part of analytics and data science. The worldwide big data market is expected to expand by $103 billion by 2027, as per a report by Statista. This burgeoning trend highlights a growing dependence on data-informed decision-making and the importance of hypothesis testing.
This blog will cover what is hypothesis testing, explore types of hypothesis testing, and illustrate how data science courses can allow you to enhance upon these skills.
What is Hypothesis Testing?
To answer the fundamental question, what is hypothesis testing? - We can describe it as a statistical technique used to make inferences or decisions based on data. In a nutshell, hypothesis testing is the process of formulating a hypothesis (an assumption or a claim) about a population parameter and then testing that hypothesis with sample data.
How does it work?
- Formulate Hypothesis: Start with a null hypothesis H₀ and an alternative hypothesis H₁. More often than not, the null hypothesis will assume no effect or no difference, while the alternative hypothesis will present the opposite.
- Data Collection: You will collect data pertaining to the hypothesis.
- Data Analysis: You will conduct the appropriate statistical tests so that you can determine whether your sample data accepts the null hypothesis or offers enough evidence to reject it.
- Drawing Conclusions: From the statistical analysis, you either reject or do not reject the null hypothesis.
Assume, for example, you are testing whether a new medicine is more potent than the current one. The null hypothesis would be that there is no greater effect of this new medicine than the one that is common, whereas the alternative hypothesis suggests that there is.
Types of Hypothesis Testing
What are the types of hypothesis testing? A variety of hypothesis tests exist, and different methods are used based on the data and research question. Different types of hypothesis tests come with their own set of assumptions and applications.
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Z-Test
A Z-test is used if the sample size is huge enough such that (n > 30) and population variance is known. It is most frequently used to check if the average value of the samples is equal to the population mean given the population follows a normal distribution.
Suppose you wanted to know whether the average salary for employees in your company has risen compared to last year, and you knew your population standard deviation—you would use a Z-test.
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T-Test
When the sample size is small (n < 30) or when population variance is unknown, a T-test is used. There are two types of T-tests:
- One-sample T-test: The test is applied to know whether the mean of the sample is different from known population mean.
- Two-sample T-test: This test compares the means of two independent samples.
T-test can be used when comparing results scores obtained by two different groups of students: one who used traditional learning methods and the other is using new educational application.
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Chi-Square Test
A Chi-square test is applied on categorical data to ascertain whether there is a significant association between two variables. For instance, a company would use the Chi-square test to establish whether customer satisfaction is related to the location of the store.
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ANOVA (Analysis of Variance)
ANOVA is utilized if more than two groups are being compared to find whether at least one mean differs significantly from the others. Its application can be represented by an example when determining whether a variety of marketing strategies result in differences in customer engagement by region.
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F-Test
An F-test is used for comparing two population variances. The test is applied in conjunction with ANOVA to check whether all group variances are equal.
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Non-Parametric Tests
If the assumptions related to a normal distribution are not satisfied, we resort to non-parametric tests, such as the Mann-Whitney U test or the Wilcoxon signed-rank test. They work well for ordinal data or skewed distributions.
Each of these types of hypothesis testing applies to a different specific use case, depending on the data at hand. The right test ensures that your results will be valid and reliable.
Why is Hypothesis Testing Important in Data Science
Application of hypothesis testing across various industries signifies its importance in data science. For example, in the healthcare industry hypothesis testing is used to verify whether a treatment or procedure, which may have been administered, was actually effective. In finance, it is applied while assessing the risk models, whereas in marketing, its use helps in estimating the effectiveness of campaigns.
For example, using hypothesis testing, a data scientist at an e-commerce company can determine if a new recommendation algorithm will increase sales. Instead of assuming that the perceived revenue increase would be caused by the algorithm, through the use of hypothesis testing, the company can determine statistically whether the variation seen was due to the algorithm or was really just a variation based on chance.
Benefits of Data Science Courses
According to Glassdoor, there are currently over 32,000 data science job openings in India. And hypothesis testing is one of the skills for data scientists which is looked upon by employers. A strong foundation in data science is needed to learn about hypothesis testing and put it into effective practice. And this is what makes enrolling in a data science course valuable. Whether you are a beginner or a professional, joining a data science course means gaining an edge in the mastery of hypothesis testing and other techniques related to data handling.
Conclusion
Essentially, hypothesis testing is a crucial statistical tool that is employed to test assumptions so as to make data-based decisions. Whether it is to compare the efficiency of marketing campaigns, testing new business strategies, or even machine learning models, hypothesis testing is an important tool because any conclusion reached must be based on data, not assumptions. By learning hypothesis testing, you not only enhance your analytical skills but also set yourself up for success in a world increasingly driven by data.
