P-values and significance in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the p-value from a t-test.

Data Analysis Python

from scipy import stats
sample1 = [5, 6, 7, 8, 9]
sample2 = [5, 5, 6, 7, 8]
t_stat, p_value = stats.ttest_ind(sample1, sample2)
print('P-value:', [1])

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

Asample1

Bp_value

Ct_stat

Dstats

Attempts:

3 left

💡 Hint

Common Mistakes

Using the t-statistic instead of the p-value.

Printing the function instead of the variable.

The p-value is the second value returned by ttest_ind. It tells us the probability of observing the data if the null hypothesis is true.

2fill in blank

medium

Complete the code to check if the p-value is less than the significance level 0.05.

Data Analysis Python

p_value = 0.03
if p_value [1] 0.05:
    print('Reject the null hypothesis')
else:
    print('Fail to reject the null hypothesis')

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

A<

B>=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using > instead of < for the comparison.

Checking equality instead of less than.

If the p-value is less than 0.05, we reject the null hypothesis, meaning the result is statistically significant.

3fill in blank

hard

Fix the error in the code to correctly calculate the p-value for a paired t-test.

Data Analysis Python

from scipy import stats
before = [20, 21, 19, 22, 20]
after = [22, 23, 20, 24, 21]
t_stat, p_value = stats.ttest_rel(before, [1])
print('P-value:', p_value)

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

Aafter

Bstats

Cbefore

Dttest_rel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same sample twice.

Passing the function name instead of data.

The paired t-test compares two related samples. The second argument should be the 'after' sample.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their lengths, but only include words longer than 3 letters.

Data Analysis Python

words = ['apple', 'cat', 'banana', 'dog', 'pear']
lengths = { [1]: len([2]) for word in words if len(word) > 3 }

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Aword

Bwords

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using the list 'words' instead of the variable 'word'.

Using 'len' as a key instead of the word.

The dictionary comprehension uses 'word' as the key and calculates the length of 'word' as the value.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths, including only words with length greater than 4.

Data Analysis Python

words = ['apple', 'cat', 'banana', 'dog', 'pear']
result = { [1]: [2] for word in words if len(word) [3] 4 }

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Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase.

Using wrong comparison operator.

The dictionary comprehension uses the uppercase version of the word as the key, the length as the value, and filters words longer than 4 letters.