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# Descriptive and Inferential Stats

SciPy’s `scipy.stats` module provides tools for **both** descriptive and inferential statistics,  
making it a complete toolkit for analyzing and drawing conclusions from data.

<br/>

## Descriptive Statistics

Descriptive stats summarize and describe data.  
Key functions in `scipy.stats` include:

- **Mean, Median, Mode** – Measures of central tendency.
- **Variance & Standard Deviation** – Spread of the data.
- **Skewness & Kurtosis** – Shape characteristics of the distribution.
- **Percentiles & Quartiles** – Data position within the distribution.

Example:  
```python
from scipy import stats
data = [2, 4, 6, 8, 10]
mean_value = stats.tmean(data)
```

<br/>

## Inferential Statistics

Inferential stats let you **make predictions or test hypotheses** about a population from a sample.
Common tools in SciPy include:

* *T-tests* – Compare means between groups.
* *Chi-Square Tests* – Test relationships between categorical variables.
* *ANOVA* – Compare means across multiple groups.
* *Correlation Tests* – Measure relationships between variables.

Example:

```python
group1 = [1, 2, 3, 4, 5]
group2 = [2, 3, 4, 5, 6]
t_stat, p_val = stats.ttest_ind(group1, group2)
```

<br/>

## Why It Matters

Understanding both descriptive and inferential statistics is essential for any data analysis workflow.
Descriptive stats help **understand your data**, while inferential stats help **make informed decisions**.

<br/>

## What’s Next?

In the next lesson, we’ll see **how SciPy is applied in various scientific tasks** beyond statistics.

Inferential statistics allow you to make educated guesses or test hypotheses about larger populations based on data from samples. This is crucial for making decisions or predictions in scientific research and data analysis, providing insights beyond merely summarizing the data.

Descriptive and Inferential Stats

Descriptive Statistics

Inferential Statistics

Why It Matters

What’s Next?

What is the primary purpose of inferential statistics?