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Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
The tyranny of averages is a phrase used in applied statistics to describe the often overlooked fact that the mean does not provide any information about the shape of the probability distribution of a data set or skewness, and that decisions or analysis based on only the mean—as opposed to median and standard deviation—may be faulty.
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
The law of averages is the commonly held belief that a particular outcome or event will, over certain periods of time, occur at a frequency that is similar to its probability. [ 1 ] [ 2 ] Depending on context or application it can be considered a valid common-sense observation or a misunderstanding of probability.
This type of filter (a moving average) shifts the data to the right. The moving average price at a given time is usually much different than the actual price at that time. Differences in real-world measured data from the true values come about from by multiple factors affecting the measurement.
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
The sample mean and sample covariance are not robust statistics, meaning that they are sensitive to outliers. As robustness is often a desired trait, particularly in real-world applications, robust alternatives may prove desirable, notably quantile-based statistics such as the sample median for location, [4] and interquartile range (IQR) for ...
The GRIM test is straightforward to perform. For each reported mean in a paper, the sample size (N) is found, and all fractions with denominator N are calculated. The mean is then checked against this list (being aware of the fact that values may be rounded inconsistently: depending on the context, a mean of 1.125 may be reported as 1.12 or 1.13).