As bias effects all stages of scientific research, scientists should actively contemplate common falterings and look to minimize or at least acknowledge their presence. Bias must be considered in experimental design and implementation as to collect accurate data. Such forethought allows for a closer representation of the intended variable: hence, decreasing the scope of confounding variables while increasing reproducibility. Appropriate statistical methods deal with the type of variables measured and provide analysis. One area of bias I did not readily think of was hidden within the presentation of those statistics.
For example, I found the traffic data in the snow intriguing. Google maps advertises the feature as a source traffic information. Therefore, we interpret the data as a measure of how many cars are on the roads, presented in ordered categories of increasing traffic being clear, orange or red. The raw data includes location and the output (speed in which iPhones on the given road are moving in relation to the speed limit). The colors then serve as a proxy where the correlation to traffic is an inference from the data. As you mentioned in the snow, the slow movement of iPhones translates to a high traffic reading. In this case, the data is still accurate (because it’s measuring iPhone motion); however, due to the limitation of this model, the outcome variable not longer directly correlated to the interpreted conclusion.
If not given access to the data, information can be lost within the interpretations and representations of statistics. Although the published work may be accurate, if readers analyze the work further, questions of bias and inherent limitations might arise. I am a proponent of open sharing of all data when possible (as suggested in this article). This along with the proposed more extensive methods section not only gives scientists the ability to critique the data and conclusions, but may also generate conversations pertaining to mistakes, bias, and limitations.
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