Reproduce the Fisher's exact test for the mammography study.
What is the p-value given the observed data?
(Please enter the value with a precision of at least 4 decimal places. Hint : Use the fisher_exact function in SciPy with the appropriate values. )
unanswered
At the conventional significance level of \alpha = 0.05, do we reject H_0that there is no treatment effect?
Yes
No
To calculate the p-value for Fisher's exact test in the mammography study, we need the observed data. As the observed data is not provided in the question, we cannot calculate the p-value or determine if we reject the null hypothesis.
To reproduce the Fisher's exact test for the mammography study and compute the p-value, you can follow these steps:
1. Collect the observed data in a contingency table. Let's denote the data as follows:
| Treated | Not Treated
------------------------------------
Mammogram + | a | b
Mammogram - | c | d
2. Use the fisher_exact function in the SciPy library to calculate the p-value. This function computes the two-tailed p-value for a 2x2 contingency table.
3. Make sure to provide the appropriate values to the fisher_exact function. In this case, you would provide the counts for each cell: a, b, c, and d.
4. The p-value obtained from the fisher_exact function will provide you with the level of statistical significance for the observed data. Make sure to round the p-value to at least 4 decimal places.
To determine whether to reject the null hypothesis (H0) at the conventional significance level (alpha = 0.05), compare the p-value obtained with alpha.
If the p-value is less than alpha (p-value < alpha), then you reject the null hypothesis, indicating there is a treatment effect.
If the p-value is greater than or equal to alpha (p-value >= alpha), then you fail to reject the null hypothesis, suggesting there is no treatment effect.
Please provide the specific values for a, b, c, and d so that I can help you calculate the p-value and determine if the null hypothesis should be rejected.