Control Groups

Correct Answer: Isolate a segment of the team, change the external factor for them and compare the productivity with the rest of the team.

The best way to identify an impact from an external factor is to isolate a segment of the team, change the external factor for them, and compare the productivity with the rest of the team. This essentially creates a control group (the rest of the team) and a test group (isolated segment). The difference in productivity can thus be attributed to the change in external factor if all other conditions remain the same. The other answers are not as effective for the following reasons: - Changing the team and leaving the external factor affecting all can't produce a valid comparison, as the effect isn't identified with a control group. - Changing the external factor without prior isolation doesn't allow for a clear identification of the factor's specific impact, as it will be hard to identify whether changes in productivity are due to the changed factor or other variables. - Changing the entire team's external factor in an attempt to raise productivity doesn't allow for a comparison. A control group isn't established in this situation, so it will be challenging to attribute changes in productivity to the external factor alone.

Correct Answer: Implement revised algorithms on a subset of data (control group) and compare the results with outputs from existing algorithm.

In the given scenario, the best approach to address issues with algorithm accuracy is to implement revised algorithms on a subset of the data (a control group) and compare the results with the outputs from the existing algorithm. This method allows for proper evaluation of the performance and accuracy of the new algorithm without affecting the entire dataset. It also ensures that you have a basis for comparison to verify if the revised algorithm is indeed working better than the existing one Discarding the current algorithm completely and starting from scratch is not advisable as it disregards the work that has already been done and could also lead to loss of useful information. Additionally, it's wasteful and inefficient in terms of time, resources, and effort Implementing the new algorithm on the whole dataset without initial tests is also not a good practice. It's risky because it could lead to complications or errors that could impact the entire dataset, especially if it turns out that the new algorithm is not accurate or has other issues Ignoring the issue and carrying on with the existing algorithm is simply not a solution. It's a way to avoid the problem rather than solve it. Issues in software development, particularly in AI-based applications where accuracy is crucial, should be addressed promptly and appropriately to ensure optimal functionality.

Correct Answer: Have the same input data run through the AI in both groups, but only implement the developed AI mods in the experimental group, the control group operates with the original AI mods.

The best approach when testing a new AI application and studying the accuracy is to run the same input data through the AI in both control and experimental groups, but only implement the developed AI modifications in the experimental group. The control group then operates with the original AI modifications. This approach allows you to directly measure the impact of the modifications, as you can compare the results of the experimental group (with modifications) and control group (without modifications) based on the same input data. The other answers are not ideal for the following reasons: - Running different input data on the AI tool in both groups would not make it possible to directly measure the impact of modifications that are implemented in experimental group because the baseline data itself is different for both groups. - Having the AI tool used regularly by both groups without any modifications would not allow for any comparisons to be made, since neither group is testing the new AI modifications. - Running the same input data on the AI tool, but implementing the new modifications in both groups would not create a valid 'control group'. The purpose of a control group is to remain unchanged so that it can serve as a comparison point for the experimental group.