1. How could statistics be used to misrepresent facts or otherwise be misleading? Please provide an

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Statistical Analysis
Data misinterpretations and inaccuracies usually arise in statistical analysis. The
misrepresentation of data can be caused by the biases of data at the initial stage of data collection
or even sampling. It is sometimes unintentional by using poor sampling methods or intentional
like in the instances when a researcher manipulates data to attain one's desired result.
Statistics can misrepresent facts especially when the sampling methods used are
inadequate. In some cases, a sample obtained from a larger group assumes similar characteristics
on individual specimens, yet in some cases, this is not the fact. Various examples understand
different features, and these assumptions regularly cripple the statistical analysis thus
misrepresenting facts. Inadequate sampling also arises when a biased data is obtained from the
process (Elliott, Alan & Wayne, 513-550). Samples in the group data can be under-represented
by a researcher when they feel it cannot yield the desired result of the analysis. Secondly,
statistics can misrepresent facts when questions used in the collection of information are unfair.
The form of questions used in the data collection process/sampling process usually determines
the type of outcome in the statistical process. Instances when the questions are
intentionally/unintentionally modified to liberate a certainly expected response cause a
misleading fact. Gene...


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