What is the level of false discovery?

The False Discovery Rate (FDR) is a statistical prediction of how much results can be expected to be false positives. This allows scientists to analyze data and determine whether it is statistically meaningful or worthless. Depending on the type of project, there may be high tolerances for a high level of false discovery, as other findings are still valid and can be useful. Scientists usually represent a statistical analysis of their findings and discuss it in the presentation of their work. Small values ​​P indicate that data is not so meaningful because there is a low statistical probability that it is unique. For example, if someone pulls the colored balls out of the bag that contains a ball of three colors, he would expect to pull about the same number of each color. If 20 balls are drawn and 10 of them have the same color, it would be unlikely to the statistical connection. To find the value of P, a researcher could perform a statistical analysis to determine how likely to draw 10 ballsFor the same color in the draw of 20 balls.

In the case of false discovery, there is greater indulgence than with p-value. Rather than looking at the statistical probability that the results are truly unique, it examines the number of false positives that are probably found in the results. A high number of false positives could still bring useful data. Scientists will have to be able to identify and exclude false positives from their results, but the remaining information could be very important.

Numer calculations can be used to determine the level of false discovery. If scientists find that this rate is high when they set an experiment, they can make some adjustments to check it. This could include changes in the study methodology, such as obtaining a larger sample to reduce the number of false positives. Careful design of the study is very important because errors in this process could cause problems with experiment.

Computer programs that help calculate the speed of false discovery. They can also be done manually. During the development of the study methodology, scientists can perform some calculations to identify obvious shortcomings in the proposal before continuing the experiment. This can help them find weaknesses and address them to be the most powerful and as useful as possible.