difference between the percentage average confidence rating for the correct items and the percentage average confidence rating for the incorrect item. Higher positive numbers indicate greater discrimination and are better scores. Q280 Your calibration is {Invalid Expression}/30) - (${gr://SC2/Score}/30),2)) * 100 }.Calibration is defined as the difference between the percentage average confidence rating and the percentage of correct answers. A score of zero is perfect calibration. Positive numbers indicate overconfidence and can go up to 100. Negative numbers represent under-confidence and can go down to -100. Your discrimination score is -{Invalid Expression}/${e://Field/RAns}) * 100),2) }.Discrimination is defined as the difference between the percentage average confidence rating for the correct items and the percentage average confidence rating for the incorrect item. Higher positive numbers indicate greater discrimination and are better scores. Q97 Thank you for working through these training exercises. We believe the CHAMP guidelines will help you in the forecasting tournament. Unfortunately, research suggests that training exercises of this sort often don’t as long as intended. The fact is that it will be hard to apply these somewhat abstract “stick” lessons to the specific forecasting problems you’ll be grappling with. For these reasons, we will periodically be in touch with reminders and tips about how to apply the CHAMP guidelines. Again, keep in mind the following acronym, CHAMP, whenever you make a forecast. Comparison classes Historical trends Averaging Mathematical models Predictable biases Finally, we close on the note of humility. As we noted at the outset, we do not claim to know all the answers and we recognize there is a great deal of expertise in our participant pool. We welcome suggestions for how to improve our training guidelines and, once again, want to encourage you to send us any constructive comments you might have to . – Example suggestions for Team condition
difference between the percentage average confidence rating for the correct items and the percentage average confidence rating for the incorrect item. Higher positive numbers indicate greater discrimination and are better scores. Q280 Your calibration is {Invalid Expression}/30) - (${gr://SC2/Score}/30),2)) * 100 }.Calibration is defined as the difference between the percentage average confidence rating and the percentage of correct answers. A score of zero is perfect calibration. Positive numbers indicate overconfidence and can go up to 100. Negative numbers represent under-confidence and can go down to -100. Your discrimination score is -{Invalid Expression}/${e://Field/RAns}) * 100),2) }.Discrimination is defined as the difference between the percentage average confidence rating for the correct items and the percentage average confidence rating for the incorrect item. Higher positive numbers indicate greater discrimination and are better scores. Q97 Thank you for working through these training exercises. We believe the CHAMP guidelines will help you in the forecasting tournament. Unfortunately, research suggests that training exercises of this sort often don’t “stick” as long as intended. The fact is that it will be hard to apply these somewhat abstract lessons to the specific forecasting problems you’ll be grappling with. For these reasons, we will periodically be in touch with reminders and tips about how to apply the CHAMP guidelines. Again, keep in mind the following acronym, CHAMP, whenever you make a forecast. Comparison classes Historical trends Averaging Mathematical models Predictable biases Finally, we close on the note of humility. As we noted at the outset, we do not claim to know all the answers and we recognize there is a great deal of expertise in our participant pool. We welcome suggestions for how to improve our training guidelines – and, once again, want to encourage you to send us any constructive comments you might have to . Example suggestions for Team condition