Q: To make sure that a few unusual responses don’t skew results or lead to inaccurate judgements, a data analyst focuses on what element of the data collection?
or
Q: What aspect of the data collection should a data analyst concentrate on to ensure that a few outlier replies don’t distort findings or result in incorrect judgments?
- Statistical significance
- Sample size
- Visualization
- Data cleaning
Explanation: A data analyst places a strong emphasis on the aspect of data gathering known as outlier identification and management. This is done so that the findings of the study are not skewed by a small number of replies that are not typical and to prevent false judgments from being made. Outliers are data points that dramatically deviate from the bulk of the dataset, and they have the potential to have a disproportionately large influence on statistical studies and machine learning models. Finding any outliers in the data and adequately dealing with them is very necessary to protect the honesty and trustworthiness of the research and avoid skewed findings. The impact that outliers have on the total dataset may be mitigated by the use of strategies such as data transformation, truncation, or imputation, amongst others.
