Accuracy measures how close measurements are to the "correct" value, and is a stronger statement than precision, as it includes both random and systematic errors. To assess accuracy the true result must already be known.
First, accuracy is related to systematic error result, and tells you how close you are to the “true” values, or the values you are looking for.
Accuracy errors arising from hysteresis, that is a deviation of the sensor's output at a specified point of the input signal when it is approached from the opposite direction, and nonlinearity, which is the maximum deviation of a real transfer function from the approximation straight line.
Accuracy reflects how close the measured value is to the actual value. Precision reflects how close the values in a set of measurements are to each other. Accuracy is affected by the quality of the instrument or measurement. Percent error is a common way of evaluating the accuracy of a measured value.
Accuracy describes how closely a given measurement matches the true value. Precision describes the ranges of measured values and is closely related to deviation and standard deviation. Measurement error is the difference between a measured value, derived from the sample, and the true population value.
Type II Error
This is the incorrect retaining of a false Null Hypothesis (Ho). This is synonymous to when the system ignore the possibility that it might not have retrieved some document which are relevant. This type of error leads to False Negative (FN).
The standard error is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation.
Accuracy measures how close measurements are to the "correct" value, and is a stronger statement than precision, as it includes both random and systematic errors. To assess accuracy the true result must already be known.
We often use percent error to describe the accuracy of a measurement.
Accuracy Versus Error
An accuracy is a qualitative form, meaning no exact value or measurement result is presented, only a presentation (usually in percentage form) of how good or bad or how far and near but no exact value, while error shows the absolute value or actual value.
Accuracy has two definitions: More commonly, it is a description of only systematic errors, a measure of statistical bias of a given measure of central tendency; low accuracy causes a difference between a result and a true value; ISO calls this trueness.
Four types of errors arise due to the classification of errors in measurement. These include systemic, random, limiting, and gross errors. Systemic errors can be divided into three groups such as observational, instrumental, and environmental errors.
An error is considered systematic if it consistently changes in the same direction. For example, this could happen with blood pressure measurements if, just before the measurements were to be made, something always or often caused the blood pressure to go up.
The standard deviation measures the precision of a single typical measurement. It is common experience that the mean of a number of measurements gives a more precise estimation than a single measurement.
Percent error is the accuracy of a guess compared to the actual measurement. It's found by taking the absolute value of their difference and dividing that by actual value.
Accuracy = correctness + precision
Correctness is the approximation of the measured value to the true value, while precision essentially means the repeatability of the measurement result under identical conditions. Thus, a precise measuring instrument displays the same value for each measurement.
In statistics, a Type I error means rejecting the null hypothesis when it's actually true, while a Type II error means failing to reject the null hypothesis when it's actually false.
A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
A type I error occurs when in research when we reject the null hypothesis and erroneously state that the study found significant differences when there indeed was no difference. In other words, it is equivalent to saying that the groups or variables differ when, in fact, they do not or having false positives.
So the device measurement is not accurate due to the apparatus. These errors are categorized into three type's namely absolute error, relative error, and percentage error. The absolute error can be defined as the variation between the values of actual and measured.