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Social science research is particularly prone to observer bias, so it is important in these fields to properly blind the researchers. In some cases, while blind experiments would be useful, they are impractical or unethical. Blinded data analysis can reduce bias, but is rarely used in social science research. [39]
This is an example of observer bias, due to the fact that the expectations of von Olson, the horse's owner, were the cause of Clever Hans actions and behaviours, resulting in faulty data. [ 7 ] One of the most notorious examples of observer bias is seen in the studies and contributions of Cyril Burt , an English psychologist and geneticist who ...
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
The field of statistics, where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision. A measurement system can be accurate but not precise, precise but not accurate, neither, or both.
The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [13] by Abraham Wald in the context of sequential tests of statistical hypotheses. [14]
Another motivation for this form of sensitivity analysis occurs after the experiment was conducted, and the data analysis shows a bias in the estimate of g. Examining the change in g that could result from biases in the several input parameters , that is, the measured quantities, can lead to insight into what caused the bias in the estimate of g .
A variable in an experiment which is held constant in order to assess the relationship between multiple variables [a], is a control variable. [2] [3] A control variable is an element that is not changed throughout an experiment because its unchanging state allows better understanding of the relationship between the other variables being tested.