What do you do if logical reasoning fails during data analysis in HR Operations?

2 Review your data analysis process

The second step to address logical reasoning failures is to review your data analysis process. Data analysis process is the way you transform, manipulate, and interpret your data, and a flawed process can lead to inaccurate or incomplete results, as well as bias or confusion. To review it effectively, you should define a clear and relevant research question and hypothesis, choose the right data analysis methods and tools for your data type and question, apply them correctly according to best practices, standards, and ethical principles, and validate and test your results to ensure they are reliable, robust, and reproducible.

3 Compare your data analysis results

The third step to address logical reasoning failures is to compare your data analysis results. Data analysis results refer to the findings, insights, and recommendations you derive from your data. When your results do not match your hypothesis, expectations, or common sense, a logical reasoning failure has occurred. To compare your data analysis results, you should evaluate them against your hypothesis and expectations to determine if they are consistent, contradictory, or surprising. Additionally, compare your results with other sources of information and evidence to determine if they are aligned, different, or complementary. Finally, seek feedback and input from other stakeholders and experts to understand their perspective on the results; are they supportive, critical, or indifferent?

4 Learn from your data analysis experience

The fourth step to address logical reasoning failures is to learn from your data analysis experience. This includes gaining knowledge, skills, and attitudes from conducting data analysis. You should identify the causes and consequences of your logical reasoning failure, document and communicate your data analysis process and results, reflect and act on your data analysis feedback and input, and update and enhance your data analysis knowledge and skills. Furthermore, ask yourself what went wrong, what went right, how were the data collected, analyzed, and interpreted? What did you find, learn, and recommend? Additionally, consider what others said about your data analysis and how can you incorporate their feedback into your practice? Finally, ask yourself what new concepts, methods, or tools were used or needed during the process? How can you learn more about them and apply them to HR operations?

5 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?