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Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
The level of measurement – known as the scale, index, or typology – will determine what can be concluded from the data. A yes/no question will only reveal how many of the sample group answered yes or no, lacking the resolution to determine an average response. The nature of the expected responses should be defined and retained for ...
In one common crossed study, 10 parts might each be measured two times by two different operators. The ANOVA then allows the individual sources of variation in the measurement data to be identified; the part-to-part variation, the repeatability of the measurements, the variation due to different operators; and the variation due to part by operator interaction.
measurement resolution, be it spatial, temporal, or otherwise; curve fitting, typically for linearity, which justifies interpolation between calibrated reference points; robustness, or the insensitivity to potentially subtle variables in the test environment or setup which may be difficult to control
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a quantity measured on an interval or ratio scale.. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation.
Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).