Understatnding Ttest
We had already explored TTest and its role in understanding the statistical significance of a distributions mean. For a ttest to have a menaingful result, the distrivutions must satisfy the following conditions:

 The data sets must be normally distributed, i.e, the shape must resemble a bell curve to an extent
 are independent and continuous, i.e., the measurement scale for data should follow a continuous pattern.
 Variance of data in both sample groups is similar, i.e., samples have almost equal standard deviation
Today, we will deep dive into these to understands why these conditions are necessary. But first, le us understand what a TDistribution is
T Distribution
 The tdistribution, also known as the Student’s tdistribution, is a probability distribution that is similar in shape to the standard normal distribution (bellshaped curve).
 The key feature of the tdistribution is that it has heavier tails compared to the normal distribution. The shape of the tdistribution depends on a parameter called degrees of freedom (df).
 As the sample size increases, the tdistribution approaches the standard normal distribution.
 In hypothesis testing with the ttest, the tdistribution is used as a reference distribution to determine the critical values for a specified level of significance (alpha) and degrees of freedom.
Normal distribution (zdistribution) is essentially a special case of t distribution. But whats important for us are certain properties that are common to both but is more prominent in the normal ditribution