Week 2 – Sept 22: T-Test

T Test

Today, Im exploring T-test and its significance. T test is a type of hypothesis testing and its a very important tool in data science. A hypothesis is any testable assupmtion about the data set and hypothesis testing allows us to validate these assumptions

T-test is predominantly used to understand whether the differrence in means of two datasets have any statistical significance. For T test to provide any meaningful insights, the datasets has to 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

Hypotheses:

• • H0: There is a significant differrence between the means of the data sets
  • H1: There is no significant differrence between the means of the data sets

T Test code

Results:

Reject the null hypothesis. There is a significant difference between the datasets.
T-statistic: -8.586734600367794
P-value: 1.960253729590773e-17

Note:

This T-test does not provide any meaningful insights as two of the requisite conditions are violates

• 1. The datasets are not normally distributes
  2. the variances of the datasets are not quite similar