Statistics, as a subject, helps us to analyse a wide range of data. Hypothesis testing is a part of Statistics, which has been created to justify or refute assumptions even when no scientific theory exists. These tests can be used to ascertain the correctness of seemingly abstract and qualitative statements. As an example we can use the tests to determine if the number of men suffering from nightmares is more than women, or determining that smoking causes lung cancer. Hypothesis testing plays an important role in making inferences. As a result, these tests are carried out in other branches of studies to arrive at scientific conclusions. To carry out these tests, data is required. When selecting the data, which is usually a sample, utmost care must be taken to ensure that the sample data selected is as representative, of the total population of data, as is practically possible. If the data in the sample does not represent the total population, the hypothesis testing will not yield proper or desired results. There are a variety of techniques that statisticians use to ensure that the data collected is representative of the overall population. There are several hypothesis testing methods. The most common among them are the Chi-squared test, the ‘T’ test, the Z test and the F test. The data is studied to determine which of these tests would be most suitable for it. Typically the testing process involves an initial assumption. Following this analysis is carried out on the data using one of the tests, to determine if the initial assumption is valid. Hypothesis testing has been useful in many spheres of life. However, the results arrived at are required to be used with a degree of caution as incorrect...