Descriptive and Inferential Stats

The scipy.stats module provides tools for both descriptive and inferential statistics — giving you everything you need to analyze data and draw meaningful conclusions.

Descriptive statistics summarize and describe the key features of your dataset.

Some useful functions in scipy.stats include:

• Mean, Median, Mode – show the data’s center
• Variance and Standard Deviation – describe how spread out the data is
• Skewness and Kurtosis – reveal the shape of the distribution
• Percentiles and Quartiles – show how values rank within the dataset

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from scipy import stats

data = [2, 4, 6, 8, 10]
mean_value = stats.tmean(data)
variance = stats.tvar(data)
std_dev = stats.tstd(data)

print("Mean:", mean_value)
print("Variance:", variance)
print("Standard Deviation:", std_dev)

Inferential statistics go beyond description — they let you *make predictions and test hypotheses about a population based on a sample of data.

Common tools in SciPy include:

• T-tests – compare means between two groups
• Chi-Square Tests – explore relationships between categorical variables
• ANOVA – compare means across three or more groups
• Correlation Tests – measure the strength of relationships between variables

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group1 = [1, 2, 3, 4, 5]
group2 = [2, 3, 4, 5, 6]

t_stat, p_val = stats.ttest_ind(group1, group2)

print("t-statistic:", t_stat)
print("p-value:", p_val)

• Descriptive statistics help you summarize your dataset.
• Inferential statistics help you test hypotheses and draw conclusions from it.

Together, they form the core foundation of data analysis — one describes what you have, and the other explains what it means.