The formula to calculate the False Discovery Rate (FDR) is:
\[ FDR = \frac{FD}{T} \times 100 \]
Where:
Let's say the number of false discoveries (\( FD \)) is 5 and the number of tests performed (\( T \)) is 100. Using the formula:
\[ FDR = \frac{5}{100} \times 100 \]
We get:
\[ FDR = 5 \% \]
So, the False Discovery Rate (\( FDR \)) is 5%.
The False Discovery Rate (FDR) is a metric used to indicate the proportion of false discoveries among the total number of tests performed. It is commonly used in multiple hypothesis testing to control the expected proportion of incorrect rejections of the null hypothesis. A lower FDR indicates a more reliable set of results.
Definition: The false discovery rate (FDR) is the expected proportion of false positives among all significant tests.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{5}{5 + 20} \)
Definition: Estimating the false discovery rate involves calculating the expected proportion of false positives among significant results.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{3}{3 + 15} \)
Definition: The false discovery rate method controls the expected proportion of false positives among significant results.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{4}{4 + 25} \)
Definition: Interpreting the false discovery rate involves understanding the proportion of false positives among significant results.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{2}{2 + 10} \)
Definition: An acceptable false discovery rate is a threshold below which the proportion of false positives is considered tolerable.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{1}{1 + 9} \)
Definition: A good false discovery rate is a low proportion of false positives among significant results, indicating high reliability.
Formula: \( \text{FDR} = \frac{V}{V + S} \)
Example: \( \text{FDR} = \frac{2}{2 + 18} \)
