resolution

two

Dynamic error: It is the difference between the true value of the quantity changing with time & the value indicated by the measurement system if no static error is assumed. It is also called measurement error.

Least count . smallest value that measured by an instrument is called least count. Resolution is the smallest increment a tool can detect and display.

Sensitivity is an absolute quantity, the smallest absolute amount of change that can be detected by a measurement. This means that at 1 volt the equivalent measurement is 1000 units or 1 mV equals one unit. However the sensitivity is 1.9 mV p-p so it will take two units before the input detects a change.

An international group of researchers under the leadership of Andreas Riedo and Niels Ligterink at the University of Bern have now developed ORIGIN, a mass spectrometer which can detect and identify the smallest amounts of such traces of life.

Accuracy is the proportion of true results, either true positive or true negative, in a population. It measures the degree of veracity of a diagnostic test on a condition. The numerical values of sensitivity represents the probability of a diagnostic test identifies patients who do in fact have the disease.

In pattern recognition, information retrieval and classification (machine learning), precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved.

Mathematically, this can be stated as:

1. Accuracy = TP + TN TP + TN + FP + FN. Sensitivity: The sensitivity of a test is its ability to determine the patient cases correctly.
2. Sensitivity = TP TP + FN. Specificity: The specificity of a test is its ability to determine the healthy cases correctly.
3. Specificity = TN TN + FP.

Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive (or true positive). This implies that there will be another proportion of actual positive cases, which would get predicted incorrectly as negative (and, thus, could also be termed as the false negative).

Sensitivity is one of four related statistics used to describe the accuracy of an instrument for making a dichotomous classification (i.e., positive or negative test outcome). Of these four statistics, sensitivity is defined as the probability of correctly identifying some condition or disease state.

Here is the short form of my mnemonics; the details below explain the logic underlying them, which should help in memorizing what they mean:

1. Accuracy: correct predictions divided by all predictions: TP+TN/(TP+FP+FP+FN)
2. Precision and Recall: focus on true positives.