How to perform T test using python?
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How to perform T test using python?

This recipe helps you perform T test using python

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Recipe Objective.

How to perform T test using python?

The t test or check (Studentâ€™s T Test) compares 2 averages (means) and tells you if they're completely or partially different from one another. The t take a look at conjointly tells you ways important the variations are; In different words it enables you to understand if those variations may have happened unintentionally.

The t score could be a magnitude relation between the distinction between 2 teams, and also the distinction among the teams. The larger the t score, the more distinction there's between teams. The smaller the t score, the more similarity there's between teams. A t score 4 times means the teams are 4 times as completely different from one another as they're among one another. After you run a t check, the larger the t-value, the more doubtless it's that the results are repeatable. A large t-score tells you that the teams are completely different. A small t-score tells you that the teams of 4 similar.

Step 1- Imporing Libraries.

``` import numpy as np import pandas as pd from scipy import stats ```

```df=pd.read_csv('/content/sample_data/california_housing_train.csv') df.head()```

Step 3- Defining two sample rows.

We will create two sample rows to measure there statistic and pvalue from the tests.

``` a = df['median_income'] b = df['median_house_value'] ```

Step 4- Applying t-test

``` t2 = stats.ttest_ind(a,b) t2 ```

The test statistic is -233.0345623005931 and the corresponding p-value is 0.0 . The p-value is less than 0.05, we reject the null hypothesis. Now we have sufficient evidence to say that this data has skewness and kurtosis which is different from a normal distribution

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