Variance measures how widely observations spread around their mean. In exploratory analysis, it helps identify whether a feature meaningfully differentiates groups or contributes little because its values barely vary. section 7.2 section 12.4
Use variance to guide practical analytical decisions:
- Compare the spread of experience across favorability or trust responses, rather than relying only on group medians. section 7.2
- Screen segmentation features before clustering; a nearly constant feature may not help distinguish customer groups. section 12.4
Experience and attitudes toward AI tools
Plot years of coding experience against favorability or trust responses with a boxenplot. A group can have a higher median while still showing substantial variance, so the distributions should be compared as well as their centers. section 7.2
fig, axis = plt.subplots()
sns.boxenplot(
data=survey_ai_users,
x="YearsCodePro", y="AISent",
color="gray",
ax=axis
)
axis.set(
title="Distribution of years of experience across
answers to the favorability question",
xlabel="Years of coding experience",
ylabel="How favorably do you view AI tools?"
)
plt.show()
Inspect the seaborn boxenplot implementation for comparing years of coding experience across AI-favorability answers. section 7.2
