Using a t-test

The main purpose of t-test statistics is to see whether there is a significant difference between the means and standard deviations of two unrelated, independent sample groups. Depending on the results, the test then determines whether the difference between the means of two independent sample groups is occurred due to a random chance. 

The first thing you need to check before thinking of applying t-test to the sample data is: the sample size of both the population sample should be less than 30

That is n<30 where n denotes the sample size of a population sample. 

Apart from the above mentioned requirements, if you are going to use t-test for your data, the following assumptions should be satisfied:

• The scale of measurement applied to the data collected should follow a continuous or ordinal scale. Example: Height of students.
• The sample made from the population is the best representative of the entire population. 
• The data should be normally distributed.
• When we plot the data on the graph, the line connecting the points should look like a bell. 
• The variance is homogeneous/equal along with the standard deviations. 

When your data satisfies all the above assumptions and requirements, you can say t-test is a best hypotheses testing tool for your study.