Researchers need to be careful when "cleaning" their data because anomalies in data can sometimes lead to important discoveries. This means that while cleaning data, which involves removing or correcting error points, can make the data more consistent and easier to analyze, it can also potentially remove interesting and significant points that deviate from the norm. These anomalies, although they may seem like errors at first, can sometimes provide new insights or lead to new hypotheses and discoveries. Therefore, researchers should be cautious not to indiscriminately remove all anomalies without first considering their potential significance.

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Researchers need to be careful when "cleaning" their data because anomalies in data can sometimes lead to important discoveries. This means that while cleaning data, which involves removing or correcting error points, can make the data more consistent and easier to analyze, it can also potentially remove interesting and significant points that deviate from the norm. These anomalies, although they may seem like errors at first, can sometimes provide new insights or lead to new hypotheses and discoveries. Therefore, researchers should be cautious not to indiscriminately remove all anomalies without first considering their potential significance.

According to Bench, researchers need to be careful when “cleaning” their data because:Group of answer choicesIt makes comparison of data more difficultAnomalies in data can sometimes lead to important discoveriesIt takes valuable time that would be better spent analysing data

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