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When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when should you use Z-test vs T-test? In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it is important to understand the difference between z-test and t-test so that you can choose the right test for your data. Let’s get started!
Difference between Z-test and T-testZ-test is a statistical hypothesis testing technique which is used to test the null hypothesis in relation to the following given that the population’s standard deviation is known and the data belongs to normal distribution:
Z = (X̄ – µ)/SE = (X̄ – µ)/σ/√n, , where SE is the standard error, X̄ is the sample mean, µ is the population mean, σ is the population standard deviation and the n is the sample size
T-test is a statistical hypothesis technique which is used to test the null hypothesis in relation to the following given the population standard deviation is unknown, data belongs to normal distribution, and the sample size is small (size less than 30)
T = (X̄ – μ) / SE = (X̄ – μ) / S/√n, where SE is the standard error, X̄ is the sample mean, µ is the population mean, S is the sample standard deviation and the n is the sample size. Note the difference between the Z-statistics and T-statistics in one-sample Z-test and one-sample T-test in relation to usage of population standard deviation σ in case of Z-test while sample standard deviation, S in case of T-test.
Other differences between the Z-test and T-test are the following:
When to use Z-test vs T-test?The following is a simplistic diagram which specifies when to use Z-test vs T-test: Note some of the following in the above diagram:
SummaryThe z-test and t-test are different statistical hypothesis tests that help determine whether there is a difference between two population means or proportions. The z-statistic is used to test for the null hypothesis in relation to whether there is a difference between the populations means or proportions given the population standard deviation is known, data belongs to normal distribution, and sample size is larger enough (greater than 30). T-tests are used when the population standard deviation is unknown, the data belongs to normal distribution and the sample size is small (lesser than 30).
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking Ajitesh KumarI have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking When the population standard deviation is unknown and the sample size is less than 30 the test statistic to use is quizlet?When the population standard deviation is unknown and the sample size n<30, the test statistic is the Student's t distribution.
What test statistic will be used if the sample size is below 30?The parametric test called t-test is useful for testing those samples whose size is less than 30. The reason behind this is that if the size of the sample is more than 30, then the distribution of the t-test and the normal distribution will not be distinguishable.
What do you do if the sample size is less than 30?For example, when we are comparing the means of two populations, if the sample size is less than 30, then we use the t-test. If the sample size is greater than 30, then we use the z-test.
What test to be used when the sample size is less than 30 and population variance is not known?A two sample t-test compares two samples of normally distributed data where the population variance is unknown and the sample sizes are small (n<30 ).
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