If the implementation of a policy initiative relies heavily on direct community collaboration and efforts, how would any type of bias be mitigated in the data collection process. For example, stores use quantitative data collection which are chosen based on the Food for Justice Leaders relationships and ability to recruit participating merchants. How might bias be rectified in the data analysis? 

 

Bias is defined as any tendency which prevents unprejudiced consideration of a question . In research, bias occurs when "systematic error is introduced into sampling or testing by selecting or encouraging one outcome or answer over others. Bias can occur at any phase of research, including study design or data collection, as well as in the process of data analysis and publication . Bias is not a dichotomous variable. Interpretation of bias cannot be limited to a simple inquisition: is bias present or not? rather must consider the degree to which bias was prevented by proper study design and implementation.

Chances and confounding can be quantified or eliminated through proper study design and data analysis. However, only the most rigorously conducted trials can completely exclude bias as an alternate explanation for an association. Unlike random error, which results from sampling variability and which decreases as sample size increases, bias is independent of both sample size and statistical significance. Bias can cause estimates of association to be either larger or smaller than the true association. In extreme cases, bias can cause a perceived association which is directly opposite of the true association.

However, bias might be rectified in data analysis through choosing the right learning model for the problem because there's a reason why all models are unique. Each problem requires a different solution and provides varying data resources. Choosing a representative data set and monitoring performance using real data.

Step-by-step explanation

Therefore, there are ways to try to maintain objectivity and avoid bias through the following :

1. Use multiple people to code the data.
2. Have participants review your results.
3. Verify with more data sources.
4. Check for alternative explanations.
5. Review the findings.