Apprendre Hypothesis Testing with Python | Statistical Analysis and Automation

Hypothesis testing is a fundamental part of research because it allows you to draw conclusions about populations based on sample data. In research, you often want to know whether an observed effect or difference between groups is statistically significant, or if it could have occurred by chance. Hypothesis testing provides a structured way to answer these questions using probability and statistical reasoning.


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import pandas as pd
from scipy import stats

# Sample research data: test scores for two groups
data = {
    "group": ["A"] * 10 + ["B"] * 10,
    "score": [88, 90, 85, 87, 91, 86, 89, 88, 90, 92,
              83, 80, 85, 82, 79, 84, 81, 83, 80, 82]
}
df = pd.DataFrame(data)

# Split data into two groups
group_a = df[df["group"] == "A"]["score"]
group_b = df[df["group"] == "B"]["score"]

# Perform independent t-test
t_stat, p_value = stats.ttest_ind(group_a, group_b)
print("t-statistic:", t_stat)
print("p-value:", p_value)

When you perform a hypothesis test, the most important value to interpret is the p-value. The p-value tells you the probability of observing your data, or something more extreme, if the null hypothesis is true. In research, a small p-value (commonly less than 0.05) suggests that the observed difference is unlikely to have occurred by chance, and you may reject the null hypothesis. A larger p-value means there is not enough evidence to conclude a significant difference between groups, so you fail to reject the null hypothesis.


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# Extracting and reporting t-statistic and p-value
result = stats.ttest_ind(group_a, group_b)
t_statistic = result.statistic
p_val = result.pvalue

print(f"Independent t-test results:")
print(f"t-statistic: {t_statistic:.3f}")
print(f"p-value: {p_val:.4f}")

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 2

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Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

Suggested prompts:

Can you explain what the t-statistic means in this context?

How do I interpret the results of the t-test for these two groups?

What is the null hypothesis in this example?

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              12345678910111213141516171819
            
import pandas as pd
from scipy import stats

# Sample research data: test scores for two groups
data = {
    "group": ["A"] * 10 + ["B"] * 10,
    "score": [88, 90, 85, 87, 91, 86, 89, 88, 90, 92,
              83, 80, 85, 82, 79, 84, 81, 83, 80, 82]
}
df = pd.DataFrame(data)

# Split data into two groups
group_a = df[df["group"] == "A"]["score"]
group_b = df[df["group"] == "B"]["score"]

# Perform independent t-test
t_stat, p_value = stats.ttest_ind(group_a, group_b)
print("t-statistic:", t_stat)
print("p-value:", p_value)


              12345678
            
# Extracting and reporting t-statistic and p-value
result = stats.ttest_ind(group_a, group_b)
t_statistic = result.statistic
p_val = result.pvalue

print(f"Independent t-test results:")
print(f"t-statistic: {t_statistic:.3f}")
print(f"p-value: {p_val:.4f}")

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 2

Hypothesis Testing with Python

1. What does a p-value represent in hypothesis testing?

2. Which scipy function is used for an independent t-test?

3. What is the null hypothesis in a t-test?

Hypothesis Testing with Python

1. What does a p-value represent in hypothesis testing?

2. Which scipy function is used for an independent t-test?

3. What is the null hypothesis in a t-test?