How do you explain your data analysis uncertainties?

1 Define your terms

Uncertainty can mean different things in different contexts. For example, uncertainty can refer to the variability or dispersion of your data, the accuracy or precision of your measurements, the confidence or credibility of your estimates, or the sensitivity or robustness of your models. To avoid confusion and misinterpretation, you should define what type of uncertainty you are talking about and how you measure it. For example, you can use standard deviation, margin of error, confidence intervals, or p-values to quantify your uncertainty.

2 Visualize your uncertainty

One of the most effective ways to show your uncertainty is to use visual aids, such as charts, graphs, or maps. Visualizing your uncertainty can help your audience grasp the magnitude, direction, and distribution of your uncertainty. For example, you can use error bars, box plots, or shaded regions to display your uncertainty around your data points, averages, or trends. You can also use color, size, or shape to indicate your uncertainty levels or categories.

3 Explain your sources

Uncertainty can arise from various sources, such as data quality, data collection, data processing, data analysis, or data interpretation. You should explain what causes your uncertainty and how you address it. For example, you can mention the limitations or assumptions of your data sources, the methods or techniques you use to clean, transform, or aggregate your data, the models or algorithms you apply to analyze your data, or the criteria or standards you follow to interpret your data.

4 Discuss your implications

Uncertainty can have implications for your findings and conclusions, as well as for your recommendations and actions. You should discuss how your uncertainty affects your results and what it means for your audience. For example, you can highlight the strengths and weaknesses of your analysis, the opportunities and risks of your conclusions, or the benefits and costs of your suggestions. You should also acknowledge the gaps or limitations of your analysis and suggest ways to reduce or resolve them.

5 Use simple language

Uncertainty can be a complex and technical topic, especially if you use jargon, acronyms, or formulas. You should use simple and clear language to explain your uncertainty to your audience, especially if they are not familiar with data analysis. You should avoid using too many numbers, symbols, or terms that might confuse or overwhelm your audience. You should also use examples, analogies, or stories to illustrate your points and make them more relatable and memorable.

6 Invite feedback

Uncertainty can be a source of dialogue and learning, rather than a source of doubt and distrust. You should invite feedback from your audience and encourage them to ask questions, share opinions, or offer suggestions. You should also be open to feedback and willing to clarify, revise, or improve your analysis. By engaging your audience in a constructive and respectful conversation, you can build trust and credibility, as well as gain new insights and perspectives.

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?