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Practice

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.


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:

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

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:

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

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.


What’s Next?​

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

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