t-test with scipy.stats in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the t-test function from scipy.stats.

Data Analysis Python

from scipy.stats import [1]

Drag options to blanks, or click blank then click option'

Attest_ind

Bttest_1samp

Cttest_rel

Dttest

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ttest' which is not a valid function in scipy.stats.

Confusing paired and independent t-test functions.

The function ttest_ind is used to perform an independent two-sample t-test.

2fill in blank

medium

Complete the code to perform an independent t-test on samples a and b.

Data Analysis Python

result = ttest_ind([1], b)

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Aa

Bb

Csample

Dresult

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same sample twice.

Passing the result variable as input.

The first argument to ttest_ind is the first sample, here named a.

3fill in blank

hard

Fix the error in the code to correctly perform a one-sample t-test on sample data.

Data Analysis Python

from scipy.stats import ttest_1samp

sample = [2, 3, 5, 6, 9]
result = ttest_1samp(sample, [1])

Drag options to blanks, or click blank then click option'

A0

BNone

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None or zero instead of the population mean.

Confusing the sample data with the population mean.

The second argument to ttest_1samp is the population mean to test against, here 5.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores t-statistics for samples with mean greater than 4.

Data Analysis Python

t_stats = {name: result.statistic for name, result in results.items() if result.statistic [1] 4 and result.pvalue [2] 0.05}

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A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators that reverse the logic.

Mixing up p-value and t-statistic conditions.

The condition checks if the t-statistic is greater than 4 and the p-value is less than or equal to 0.05.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension filtering samples with mean less than 3 and p-value less than 0.01.

Data Analysis Python

filtered = {name: result.pvalue for name, result in results.items() if result.statistic [1] 3 and result.pvalue [2] 0.01 and name.[3]('test')}

Drag options to blanks, or click blank then click option'

A<

B<=

Cstartswith

Dendswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators.

Using 'endswith' instead of 'startswith' for name filtering.

The t-statistic is checked to be less than 3, p-value less than or equal to 0.01, and the sample name starts with 'test'.