What is T-test?

T-test is a statistical test used to determine whether there is a significant difference between the means of two groups.

It is particularly useful when the sample size is small (typically n < 30) and the population standard deviation is unknown. The T-test relies on the t-distribution, which is similar to the normal distribution but has heavier tails.

Types of T-Tests

There are three types of T-test i.e.,

• One-Sample T-Test
  • Compares the sample mean to a known value (usually a population mean).
  • Used to determine if the sample comes from a population with a specific mean.
• Two-Sample T-Test (Independent T-Test)
  • Compares the means of two independent samples.
  • Used to determine if there is a significant difference between the means of two groups.
• Paired Sample T-Test (Dependent T-Test)
  • Compares means from the same group at different times (e.g., before and after a treatment) or from matched pairs.
  • Used to determine if there is a significant difference between paired observations.

Read More about T-test.

Z-tests are used when the population variance is known and the sample size is large, while t-tests are used when the population variance is unknown and the sample size is small.

This article explains the differences between Z-tests and T-tests, detailing their purposes, assumptions, sample size requirements, and applications in statistical hypothesis testing.