0A t-test helps us check if two groups have different average values. It tells us if the difference is real or just by chance.
from scipy.stats import ttest_ind, ttest_rel # Independent t-test (two separate groups) ttest_ind(sample1, sample2, equal_var=True) # Paired t-test (same group, two conditions) ttest_rel(sample1, sample2)
ttest_ind is for two independent groups (like two different classes).
ttest_rel is for paired data (like before and after measurements of the same people).
from scipy.stats import ttest_ind group1 = [20, 22, 19, 24, 30] group2 = [25, 27, 29, 24, 28] result = ttest_ind(group1, group2) print(result)
from scipy.stats import ttest_rel before = [100, 102, 98, 105, 110] after = [102, 104, 100, 108, 115] result = ttest_rel(before, after) print(result)
This program runs both an independent t-test and a paired t-test and prints the test statistic and p-value for each.
from scipy.stats import ttest_ind, ttest_rel # Independent samples example group_a = [12, 15, 14, 10, 13] group_b = [22, 25, 19, 24, 20] independent_test = ttest_ind(group_a, group_b) # Paired samples example before_treatment = [100, 102, 98, 105, 110] after_treatment = [102, 104, 100, 108, 115] paired_test = ttest_rel(before_treatment, after_treatment) print(f"Independent t-test result: statistic={independent_test.statistic:.3f}, pvalue={independent_test.pvalue:.3f}") print(f"Paired t-test result: statistic={paired_test.statistic:.3f}, pvalue={paired_test.pvalue:.3f}")
A small p-value (usually less than 0.05) means the groups are likely different.
Use ttest_ind for different groups and ttest_rel for paired data.
Check if your data meets assumptions like normal distribution for best results.
T-tests compare averages between two groups.
ttest_ind is for independent groups; ttest_rel is for paired samples.
Look at the p-value to decide if the difference is meaningful.
What is the main difference between ttest_ind and ttest_rel in SciPy?
ttest_indttest_ind is used to compare the means of two independent groups, meaning the groups have no relationship.ttest_relttest_rel is used for paired samples, where the same subjects are measured twice or matched pairs are compared.ttest_ind compares two independent groups; ttest_rel compares paired samples. -> Option Attest_ind compares two independent groups; ttest_rel compares paired samples. [OK]ttest_ind is for paired dataWhich 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 herettest_ind, which takes two arrays as arguments.stats.ttest_ind(a, b). Using only one array or the paired test ttest_rel is incorrect here.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))ttest_rel compares paired samples 'before' and 'after'. The differences are small.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)ttest_ind requires two arrays as arguments. Here only one array a is passed.ttest_ind is correct for independent samples. Different lengths are allowed. Import statement is correct.ttest_ind function. -> Option Dttest_ind function. [OK]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?
ttest_rel is designed for paired samples to compare means before and after treatment.ttest_rel because the scores are paired measurements. -> Option Bttest_rel because the scores are paired measurements. [OK]Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.