Final answer:
A model with a decision threshold lowered by 40% of its worthiness metric is likely to be less accurate and less fair, as it may result in more mistakes and potential unfairness. To maintain a small error bound with the same level of confidence, the sample size should be increased. Larger sample sizes generally lead to more accurate results and maintained confidence levels.
Step-by-step explanation:
A model that makes more mistakes by moving its decision threshold down 40% of its worthiness metric will be potentially less accurate and less fair. Decision thresholds in models are set to balance accuracy and fairness, and moving the threshold could lead to more false positives or negatives, reducing accuracy. Additionally, if the threshold shift impacts certain groups differently, it could also lead to less fairness.
If you wanted a smaller error bound while keeping the same level of confidence, you would need to increase the sample size of the study before it was done. A larger sample size would lead to a more precise estimate of the population parameter, assuming that other variables remain constant.
With reference to accuracy in measurements, assume that three darts are thrown at a dartboard. Darts that hit near the bulls-eye represent higher accuracy, while darts that land farther away represent poorer accuracy. A model or a study that is less able to predict or estimate values close to the true or accepted value is considered less accurate.
Therefore, if the level of confidence is to be kept the same while taking another survey to maintain or improve accuracy, it is necessary to increase the sample size. Conversely, if the sample size is reduced to 49 without other adjustments, the level of confidence would decrease because the sample would be less representative of the entire population.