How can you avoid process performance measurement biases?

1 What are process performance measurement biases?

Process performance measurement biases are errors or influences that can affect how data is collected, analyzed, or interpreted. Examples of such biases include confirmation bias, recency bias, selection bias, anchoring bias, and attribution bias. Confirmation bias is when you favor data that supports existing beliefs or expectations and ignore data that contradicts them. Recency bias is when you give more weight to data that is more recent or fresh in your memory. Selection bias is when you select or exclude data based on certain criteria or preferences, creating a sample that is not representative of the whole population or process. Anchoring bias occurs when you rely too much on the first piece of data encountered and adjust subsequent data or judgments based on that anchor. Attribution bias is attributing the causes or outcomes of your process performance to internal or external factors based on your own perspective or assumptions. These biases can lead to inaccurate or misleading conclusions, so it's important to be aware of them and take steps to avoid them.

2 How can you avoid confirmation bias?

Confirmation bias can make you blind to data that challenges your existing views or hypotheses, and make you overconfident in your process performance. To avoid this, it is important to seek out data that contradicts or questions your assumptions or expectations and test them with the same rigor as data that supports them. Additionally, consider alternative explanations or scenarios for your data, and evaluate them objectively and fairly. Furthermore, ask for feedback or input from others who have different perspectives or expertise and listen to their opinions and arguments. Finally, review your data and conclusions with a critical eye, looking for gaps, inconsistencies, or errors.

3 How can you avoid recency bias?

Recency bias can make you focus on data that is more recent or memorable, and neglect data that is older or less noticeable. To avoid this, maintain a consistent and regular time frame for collecting and analyzing your data, without changing it arbitrarily or based on convenience. Compare your data across different periods or cycles to look for trends, patterns, or changes over time. Additionally, balance your data with historical or benchmark data to see how your process performance compares to past performance or industry standards. It's important to avoid making hasty or impulsive decisions based on a single piece of data, and wait for more evidence before drawing conclusions.

4 How can you avoid selection bias?

Selection bias can lead to a biased or unrepresentative sample of data, and generalizing your findings to the whole population or process. To avoid this, you should first define your population or process explicitly and identify the relevant variables or characteristics that affect performance. Additionally, use a random or stratified sampling method to ensure your sample is large and diverse enough to capture the variability and complexity of your population or process. It's also important to avoid cherry-picking or excluding data based on preferences or convenience, and instead justify any inclusion or exclusion criteria with logic and evidence. Finally, check your sample for biases or imbalances, and adjust them with appropriate methods or techniques.

5 How can you avoid anchoring bias?

Anchoring bias can lead you to rely too heavily on the first piece of data or information you come across, and influence your subsequent data or judgments. To avoid this, it’s important to gather multiple sources of data or information, and compare and contrast them. You should also avoid using arbitrary or irrelevant data or information as your anchor, and instead choose a more reliable reference point. Additionally, challenge your initial data or information to look for any flaws, limitations, or assumptions that may affect its accuracy. Finally, revise your data or information as new evidence becomes available, and be open to changing your mind or perspective.

6 How can you avoid attribution bias?

Attribution bias can make you attribute the causes or outcomes of your process performance to internal or external factors, based on your own perspective or assumptions. To avoid this, it's important to analyze your process performance using a causal or logical framework and identify the factors that influence your process inputs, outputs, and outcomes. Additionally, you should avoid making causal inferences based on correlation or coincidence and look for evidence of causation. Furthermore, it's essential to not blame or credit yourself or others for your process performance and acknowledge the role of situational or environmental factors. Lastly, you should seek feedback from those who are involved or affected by your process performance and understand their views and experiences.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?