Why hypothesis testing validates claims in SciPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function for hypothesis testing from scipy.

SciPy

from scipy.stats import [1]

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

Amode

Bmean

Cmedian

Dttest_ind

Attempts:

3 left

💡 Hint

Common Mistakes

Using descriptive statistics functions like mean or median instead of a test function.

Importing a function that does not perform hypothesis testing.

The ttest_ind function is used to perform an independent t-test, which is a common hypothesis test to compare means of two groups.

2fill in blank

medium

Complete the code to perform a t-test on two sample arrays.

SciPy

stat, p_value = ttest_ind(sample1, [1])

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

Asample2

Bsample3

Csample1

Dsample4

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same sample twice.

Passing an unrelated variable.

To compare two groups, you need to pass both sample arrays to the ttest_ind function. Here, sample2 is the second group.

3fill in blank

hard

Fix the error in the code to correctly check if the p-value is less than 0.05.

SciPy

if p_value [1] 0.05:
    print('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 greater than instead of less than.

Using equality operator which is not appropriate here.

We reject the null hypothesis if the p-value is less than the significance level (usually 0.05).

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

SciPy

{word: [1] for word in words if [2] > 3}

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

Alen(word)

Bword

Dword.length

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length as the value.

Using an incorrect attribute like word.length which is not valid in Python.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is greater than 1.

SciPy

{ [1]: [2] for [3], count in word_counts.items() if count > 1 }

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

Aword.upper()

Bcount

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key without converting to uppercase.

Mixing up keys and values in the comprehension.

The comprehension maps each word converted to uppercase to its count, but only includes words with counts greater than 1.