[Side Project After Work] Big Data Analysis Certification Practical Exam (Type 1, 2, 3)
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Why is equal_var=True when the problem doesn't mention equal variance?
Why is equal_var=True when the problem doesn't mention equal variance?
Thank you to Song** for your question.
In the Type 3 Work - Subproblem 3 of the practice problem,
the term "equal variance" does not directly appear in the problem text.
However, in the solution, it is as follows:
#3
from scipy import stats
result = stats.ttest_ind(df[cond1]['Resistin'], df[cond2]['Resistin'], equal_var = True)
print(round(result.pvalue,3))I used the equal variance assumption (Student's t-test).
The reasons are as follows.
The problem was a typical three-stage testing problem structured with the following flow.
# Checking Variance Differences Between Two Groups with F-test
Calculating the Pooled Variance Estimator
Perform independent samples t-test using the pooled variance
The very statement of calculating pooled variance already presupposes the assumption that the variances of the two groups are equal.
Therefore, I approached the solution using equal_var=True.
Additionally,
Single-sample t-test: Equal variance test not required (no two groups to compare)
Paired t-test: Equal variance test not required (uses only difference values)
Independent Samples t-test: Considering Equal Variance Test