https://github.com/faraazarsath/hypothesis-testing-in-healthcare
This GitHub repository contains the analysis of drug safety data using hypothesis testing.
https://github.com/faraazarsath/hypothesis-testing-in-healthcare
chisquare-test hypothesis-testing
Last synced: 8 months ago
JSON representation
This GitHub repository contains the analysis of drug safety data using hypothesis testing.
- Host: GitHub
- URL: https://github.com/faraazarsath/hypothesis-testing-in-healthcare
- Owner: FaraazArsath
- Created: 2023-11-14T10:59:51.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-14T11:32:46.000Z (almost 2 years ago)
- Last Synced: 2025-01-09T10:06:08.791Z (9 months ago)
- Topics: chisquare-test, hypothesis-testing
- Language: Jupyter Notebook
- Homepage:
- Size: 29.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hypothesis-Testing-in-Healthcare
## 1. Proportions Z-Test:
Null Hypothesis (H 0): The proportion of adverse effects is the same in the Drug and Placebo groups.
Alternative Hypothesis (H 1): The proportion of adverse effects is different between the Drug and Placebo groups.
Results:
P-Value: p=0.9639
Interpretation:
With a p-value of 0.9639, we fail to reject the null hypothesis. There is insufficient evidence to suggest a significant difference in the proportions of adverse effects between the Drug and Placebo groups. The observed difference is likely due to random chance.
## 2. Chi-Squared Test:
Null Hypothesis (H 0): The number of adverse effects is independent of the treatment and control groups.
Alternative Hypothesis (H 1): There is a significant association between the number of adverse effects and the treatment groups.
Results:
P-Value: p=0.6150
Interpretation:
With a p-value of 0.6150, we fail to reject the null hypothesis. There is insufficient evidence to conclude that the number of adverse effects is dependent on the treatment groups (Drug and Placebo). The observed association is not statistically significant.
## 3. Mann-Whitney U Test:
Null Hypothesis (H 0): There is no significant difference in the distribution of ages between the Drug and Placebo groups.
Alternative Hypothesis (H 1): There is a significant difference in the distribution of ages between the Drug and Placebo groups.
Results:
P-Value: p=0.256963
Interpretation:
With a p-value of 0.256963, we fail to reject the null hypothesis. There is insufficient evidence to suggest a significant difference in the distribution of ages between the Drug and Placebo groups. The observed difference is not statistically significant.
## Interpretation:
### Adverse Effects Proportions:
The proportions Z-test suggests that the proportion of adverse effects is similar between the Drug and Placebo groups. The lack of statistical significance indicates that any observed differences are likely due to random variability.
### Number of Adverse Effects:
The chi-squared test indicates that the number of adverse effects is not significantly associated with the treatment groups. There is no evidence to suggest that the treatment type influences the number of adverse effects.
### Age Distribution:
The Mann-Whitney U test shows no significant difference in the distribution of ages between the Drug and Placebo groups. Age does not appear to be a differentiating factor between the two treatment groups.