Question

The C statistic is also known as the response criterion. This is the amount of evidence an individual requires to make a target-present response. What does a negative C statistic mean? What does a positive C statistic mean? What factors can influence someone’s response criterion?

single detection lab Results

The table below lists several detection statistics for different numbers of noise dots. The most important statistic is d'. It gives a measure of your ability to discriminate displays in which the target was present from displays in which the target was absent. You should find that d' decreases as the number of noise dots increases. It also reports C, a measure of bias (negative = liberal; positive = conservative).

Number of Dots	d'	C	Hits	False alarms	Correct rejections	Misses
144 dots	5.582	-1.509	1.000	0.100	0.900	0.000
400 dots	1.683	0.000	0.800	0.200	0.800	0.200
1000 dots	1.049	0.000	0.700	0.300	0.700	0.300

Answer 1

Note: This response is in UK English, please paste the response to MS Word and you should be able to spot discrepancies easily. You may elaborate the answer based on personal views or your classwork if necessary.

(Answer) A “C statistic” is when the result is affected by the presence of another element. For instance, John has to count how many flowers are there in a particular garden. When he counted during the sunny morning, there seemed to be more flowers than when he was sent to count at night. In this case, the presence of sunlight enabled John to see clearly and was able to count more flowers than at night when it was darker. Here, one statistic that is the presence of light was the influence on another statistic, which is the number of visible flowers.

This is also known as the “concordance statistic” or the “C-index” Positive and negative C-statistics depend on the predictive value of the subject. If the analysis is positively affected by the presence of the C-statistic, then it is a positive value. If the presence of the C-statistic does not increase the likelihood of a better prediction, then it is a negative outcome. For example, the increase in the number of dots above results in a decline in the d’ and correct rejections. Conversely, it increases the number of misses. This means that the number of dots has a negative effect on the prediction value.