Which method can be used to remove biases from data during analysis?

The method of data filtering is effective in removing biases from data during analysis by allowing the analyst to refine the dataset, ensuring that it contains only the relevant information and minimizing noise and irrelevant data that could skew results. This process enables a more accurate comparison and evaluation by focusing on the data points that are truly representative of the population or phenomenon being studied.

By filtering out unwanted data or aspects that could introduce bias, the analysis can yield more reliable insights and conclusions. Additionally, this method can help in addressing specific concerns related to bias that may arise from attributes such as demographic variables, ensuring that the dataset better reflects the intended analysis criteria.

Normalization techniques, while useful for standardizing data across different scales, do not directly address biases inherent in the data itself. Data sampling can help in selecting a representative subset of data, but it may not eliminate biases unless executed properly. Outlier removal focuses on eliminating extreme values but does not necessarily encompass all forms of bias that may exist in broader datasets.