One way to characterize data from multiple measurements/runs is to assume that the measurements are randomly scattered around a central value that provides the best estimate of expected, or “true” va...One way to characterize data from multiple measurements/runs is to assume that the measurements are randomly scattered around a central value that provides the best estimate of expected, or “true” value. There are two common ways to estimate central tendency: the mean and the median.
One way to characterize data from multiple measurements/runs is to assume that the measurements are randomly scattered around a central value that provides the best estimate of expected, or “true” va...One way to characterize data from multiple measurements/runs is to assume that the measurements are randomly scattered around a central value that provides the best estimate of expected, or “true” value. There are two common ways to estimate central tendency: the mean and the median.
The page discusses the characterization of data by central tendency and spread, involving measures like mean, median, range, and standard deviation. Errors affecting accuracy and precision are address...The page discusses the characterization of data by central tendency and spread, involving measures like mean, median, range, and standard deviation. Errors affecting accuracy and precision are addressed through propagation of uncertainty. The page covers probability distributions, normal distribution confidence intervals, and statistical analysis techniques such as t-tests and F-tests for comparing data sets.
