N/APosted on - 06/19/2019
In data analysis, all our prediction depends on the data we collected. What if there is a bias while collecting data. Unknowingly we may have collected false equivalence data which may result in the wrong conclusion at the end. It may lead to customer dissatisfaction and heavy losses in the future. So it is very crucial to remove bias from data.
How To Remove Bias From Data
Implementing algorithms with enough variables and having quality control will keep our data on the right track. Collecting simple random data based on chance will also remove the bias. All types of inputs are covered, and there will be no effort required for potential participants.
Stratified random sampling also helps as all types of participants/inputs have given equal importance, and its high statistical precision saves money time and effort. We can also use quota sampling, which is non-profitable sampling and has characteristics of both random sampling and stratified sampling. But it is not as accurate as it can involve random selections also.