You're faced with challenging statistical data. How do you ensure credibility while presenting it?

Ensuring credibility when presenting challenging statistical data involves several key steps: Source Verification: Use reputable and authoritative sources for your data, such as academic journals, publications. Data Accuracy: Double-check your data for accuracy. Ensure that figures are correct and calculations are precise. Transparency: Clearly explain your data collection methods and any assumptions made during analysis. Data Visualization: Use clear and appropriate visualizations (charts, graphs, tables) to make complex data more understandable. Statistical Methods: Employ appropriate statistical methods and explain why these methods were chosen. Include confidence intervals, margins of error, and other relevant statistical measures.

In my experience, I think that before any EDA or statistical tests, one has to get intimate with the data either by becoming a subject matter expert (SME) or by borrowing expertese from an SME. I ran into this problem at work when the data was modeled for a project, an extensive EDA and analysis was done to build a very large scale network analysis model. The model produced unexpected results. It turned out that it was based on flawed data sources. You know what they say, GIGO. Also, there are inherent biases in the data that the SME can uncover which may not be visible to the layman and can lead to biased models. So, after thoroughly understanding the problem and data at hand, one can start with hypotheses and inference.

Error analysis is a crucial aspect of statistical data presentation and interpretation. It involves identifying, quantifying, and mitigating errors that can affect the accuracy and reliability of your results. 1. Identifying Errors Data Collection: Ensure that the data collection methods are reliable and standardized. Cross-check the data with other sources or repeat measurements when possible. Data Entry: Use automated systems to minimize human error in data entry. Validate data entries by double-checking samples. 2. Quantifying Errors Error Bars: Use error bars in graphs to represent the variability or uncertainty in the data. Standard Deviation and Variance: Calculate these measures to quantify the spread of the data around the mean.

Often, complexities in data (assuming to have passed validation checks) can be presented credibly through contextualisation. Running through the possible scenarios that contributed to these complexities, either wholly or in part, and clearly communicating implications in an “if, then” manner would be incredibly helpful toward that end.