t-test (ttest_ind, ttest_rel) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

from scipy.stats import [1]

# Now you can use this function to compare two independent samples

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

Attest_rel

Bttest_ind

Cttest_1samp

Dttest_paired

Attempts:

3 left

💡 Hint

Common Mistakes

Using ttest_rel which is for related samples.

Trying to import a non-existent function like ttest_paired.

The function ttest_ind is used for independent t-tests, which compare two separate groups.

2fill in blank

medium

Complete the code to perform a related t-test on two paired samples.

SciPy

from scipy.stats import ttest_rel

sample1 = [5, 7, 8, 6, 9]
sample2 = [6, 8, 7, 7, 10]

result = ttest_rel([1], sample2)
print(result)

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

Asample1

Bsample2

Csample1 + sample2

Dsample1 * sample2

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the samples or using combined lists.

Using arithmetic operations instead of sample lists.

The first argument to ttest_rel is the first sample, here sample1.

3fill in blank

hard

Fix the error in the code to correctly perform an independent t-test between two groups.

SciPy

from scipy.stats import ttest_ind

group_a = [12, 15, 14, 10, 13]
group_b = [9, 11, 10, 8, 12]

result = ttest_ind(group_a, [1])
print(result)

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

A[group_a, group_b]

Bgroup_a

Cgroup_b

Dgroup_a + group_b

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same group twice.

Passing a list of groups or concatenated lists.

The second argument to ttest_ind should be the second group, group_b.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores t-test p-values for pairs of samples.

SciPy

samples = {'A': [1, 2, 3], 'B': [2, 3, 4], 'C': [3, 4, 5]}
from scipy.stats import ttest_ind

p_values = {key: ttest_ind(samples[key], samples[[1]]).pvalue for key in samples if key != [2] and key != 'C'}

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

A'B'

B'A'

C'C'

D'D'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same sample for both blanks.

Using samples not in the dictionary.

The comprehension compares each sample with sample 'B' and excludes 'A' and 'C' as specified.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores t-test statistics for pairs of samples where the statistic is greater than 2.

SciPy

samples = {'X': [10, 12, 11], 'Y': [9, 11, 10], 'Z': [13, 15, 14]}
from scipy.stats import ttest_ind

results = {k: ttest_ind(samples[k], samples[[1]]).statistic for k in samples if k != [2] and ttest_ind(samples[k], samples[[3]]).statistic > 2}

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

A'Y'

B'X'

C'Z'

D'W'

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent samples in blanks.

Using samples not in the dictionary.

The comprehension compares each sample with 'Y', excludes 'Z', and checks if the statistic is greater than 2 when compared with 'Y'.

Practice

1.

What is the main difference between ttest_ind and ttest_rel in SciPy?

easy

A. ttest_ind compares two independent groups; ttest_rel compares paired samples.

B. ttest_ind is used for paired samples; ttest_rel is for independent groups.

C. ttest_ind calculates correlation; ttest_rel calculates mean difference.

D. ttest_ind is for one sample; ttest_rel is for two samples.

Solution

1. Step 1: Understand the purpose of ttest_ind

   ttest_ind is used to compare the means of two independent groups, meaning the groups have no relationship.

2. Step 2: Understand the purpose of ttest_rel

   ttest_rel is used for paired samples, where the same subjects are measured twice or matched pairs are compared.

3. Final Answer:

   ttest_ind compares two independent groups; ttest_rel compares paired samples. -> Option A

4. Quick Check:

   Independent vs paired samples = ttest_ind compares two independent groups; ttest_rel compares paired samples. [OK]

Hint: Independent means separate groups; paired means related samples [OK]

Common Mistakes:

• Confusing which test is for paired samples
• Thinking both tests are interchangeable
• Assuming ttest_ind is for paired data

2.

Which of the following is the correct way to import and run an independent t-test on two arrays a and b using SciPy?

import scipy.stats as stats

# Your code here

easy

A. stats.ttest_ind(a, b)

B. stats.ttest_rel(a, b)

C. stats.ttest_ind(a)

D. stats.ttest_rel(a)

Solution

1. Step 1: Identify the correct function for independent samples

   The function for independent samples is ttest_ind, which takes two arrays as arguments.

2. Step 2: Check the function call syntax

   The correct syntax is stats.ttest_ind(a, b). Using only one array or the paired test ttest_rel is incorrect here.

3. Final Answer:

   stats.ttest_ind(a, b) -> Option A

4. Quick Check:

   Independent test with two arrays = stats.ttest_ind(a, b) [OK]

Hint: Use ttest_ind for two arrays, both as arguments [OK]

Common Mistakes:

• Using ttest_rel for independent samples
• Passing only one array instead of two
• Forgetting to import scipy.stats correctly

3.

What is the output of this code snippet?

from scipy.stats import ttest_rel

before = [20, 22, 19, 24, 25]
after = [21, 23, 20, 22, 26]
result = ttest_rel(before, after)
print(round(result.pvalue, 3))

medium

A. 0.900

B. 0.200

C. 0.531

D. 0.041

Solution

1. Step 1: Understand the paired t-test calculation

   The ttest_rel compares paired samples 'before' and 'after'. The differences are small.

2. Step 2: Calculate or trust the p-value output

   Running the code gives a p-value approximately 0.531.

3. Final Answer:

   0.531 -> Option C

4. Quick Check:

   Paired t-test p-value ~0.531 = 0.531 [OK]

Hint: Paired test with small differences yields high p-value [OK]

Common Mistakes:

• Confusing p-value with t-statistic
• Using ttest_ind instead of ttest_rel
• Rounding errors in p-value

4.

Identify the error in this code that tries to perform an independent t-test:

from scipy.stats import ttest_ind

a = [5, 7, 8]
b = [6, 9]
result = ttest_ind(a)
print(result.pvalue)

medium

A. Lists a and b have different lengths.

B. Using ttest_ind instead of ttest_rel.

C. Incorrect import statement for ttest_ind.

D. Missing second argument for ttest_ind function.

Solution

1. Step 1: Check function call parameters

   ttest_ind requires two arrays as arguments. Here only one array a is passed.

2. Step 2: Verify other options

   Using ttest_ind is correct for independent samples. Different lengths are allowed. Import statement is correct.

3. Final Answer:

   Missing second argument for ttest_ind function. -> Option D

4. Quick Check:

   Two arrays needed for ttest_ind = Missing second argument for ttest_ind function. [OK]

Hint: ttest_ind needs two arrays as input [OK]

Common Mistakes:

• Passing only one array to ttest_ind
• Thinking arrays must be same length for independent test
• Confusing ttest_ind and ttest_rel usage

5.

You have test scores of 10 students before and after a training program. You want to check if the training improved scores significantly. Which test should you use and why?

hard

A. Use ttest_ind because the groups are independent.

B. Use ttest_rel because the scores are paired measurements.

C. Use ttest_ind because it handles paired data better.

D. Use neither; a correlation test is needed.

Solution

1. Step 1: Identify the data type

   Scores before and after training are from the same students, so data are paired.

2. Step 2: Choose the correct t-test

   ttest_rel is designed for paired samples to compare means before and after treatment.

3. Final Answer:

   Use ttest_rel because the scores are paired measurements. -> Option B

4. Quick Check:

   Paired data needs ttest_rel = Use ttest_rel because the scores are paired measurements. [OK]

Hint: Paired before-after data use ttest_rel [OK]

Common Mistakes:

• Using ttest_ind for paired data
• Confusing correlation with mean comparison
• Ignoring the pairing in data