**What is One Sample T Test?**

It is a statistical test used to compare means of a variable with any mean that you give.

Example – You want to compare mean score of a class of 50 students with a value of 71, you can use this test to check whether the mean is significantly same or different.

There is only 1 variable used in this case and only one value used for comparing the mean with as shown:

**Variable** **–** Will always be numeric(scale). Example – Sales in INR for a particular product

**Value – **Will also be numeric(scale).

**Why do we use One Sample T-test?**

When you need to check whether actual results are same as expected results. For example, you know the actual sales for a particular product and want to check whether it is same or different as expected.

There is a particular significant value also that is given by clients. For this tutorial we will be using significant value of 0.05, meaning that results should be 95% significant.

**Example of One sample T-test**

Problem Statement: Scores for a class of 50 students are recorded for the current year. Professors expects that mean score for this class should be around 71. Considering the significant value as 0.05, is there a significant difference between actual score or expected score?

Here, scores will be our variable we want apply test on, and the value that we will be testing will be expected value – 71.

Let’s say results have level of significance of 0.045. We will conclude that there is a significant difference between the actual and the expected mean score. Here we will reject the null hypothesis.

f there is a significant difference, we say that we reject null hypothesis.

If there is no significant difference, we do not reject null hypothesis.

There are typically two types of hypothesis:

**1. Null Hypothesis** – meaning, there is no change/ everything is same.

**2. Alternate Hypothesis** – meaning, there is a change / there is a difference