Regarding your intention to calculate p-values using a t-test, I think that’s a good approach for hypothesis testing. Here’s a general outline of how to proceed:
Data Cleaning and Handling Missing Values: It’s essential to deal with missing values before performing any statistical analysis. Depending on your dataset and the nature of missing data, you can impute missing values, remove rows with missing values, or apply other strategies. Pandas is a handy library for data cleaning in Python. Example of removing rows with missing values:
cleaned_data = original_data.dropna()
Calculating T-Test for Hypothesis Testing:
To perform a t-test, you typically need to define your null and alternative hypotheses and then apply the appropriate t-test based on your research question. Here’s a general structure for conducting a t-test in Python:
from scipy.stats import ttest_ind
# Example: Testing if there’s a significant difference in obesity rates between two groups (e.g., Group A and Group B)
group_a_obesity = cleaned_data[cleaned_data[‘group’] == ‘Group A’][‘obesity_rate’]
group_b_obesity = cleaned_data[cleaned_data[‘group’] == ‘Group B’][‘obesity_rate’]
t_stat, p_value = ttest_ind(group_a_obesity, group_b_obesity)
# Print the results
print(f’T-statistic: {t_stat}’)
print(f’P-value: {p_value}’)
Replace ‘Group A’ and ‘Group B’ with the actual groups you want to compare, and ‘obesity_rate’ with the variable of interest (e.g., ‘diabetes_percentage’, ‘inactivity_level’).
Interpreting P-Values:
Once you have the p-value, you can interpret it based on your research question and the significance level (usually denoted as α or alpha) you’ve chosen. Common significance levels are 0.05 or 0.01.
Here’s a general guideline: If p-value < α: Reject the null hypothesis. There is evidence to support that there is a significant difference between the groups. If p-value ≥ α: Fail to reject the null hypothesis. There is no significant evidence of a difference between the groups.