How can you communicate uncertainty in analysis results without losing credibility?

”In God we trust. All others must bring data.” - W. Edwards Deming. But we should never trust data blindly! Having trustworthy data starts from reducing Type I and Type II errors at the data collection stage. In addition, appropriate data cleaning techniques need to be considered carefully too. As the analyst, our audience may blindly trust the data so it is our professional responsibility to highlight the uncertainties such as variance, accuracy and representativeness of the data.

Communicating uncertainty in analysis results is a crucial aspect of scientific integrity and responsible decision-making. It involves acknowledging the limitations of data, analytical methods, and assumptions, and expressing the degree of confidence in the findings. While acknowledging uncertainty may seem counter-intuitive, it actually enhances credibility by demonstrating transparency, rigor, and a commitment to truth-seeking. -Acknowledge uncertainty upfront -Quantify uncertainty when possible -Explain the sources of uncertainty -Distinguish between uncertainty and erro -Communicate the implications of uncertainty -Emphasize what is known -Use clear and concise language -Visualize uncertainty -Provide context and references

Being honest & realistic about the data and it's limitations is the first step to understanding the results. Skewing in the data set is also reality which should be accepted by the analyst and the end user of the analysis. As an analyst I would present this fact on the outset of the presentation to ensure that the data is consumed in the right sense.

To effectively communicate uncertainty in analysis results without losing credibility, it is crucial to balance transparency, accuracy, and confidence. Key strategies include acknowledging uncertainty upfront, quantifying uncertainty when possible, explaining the sources of uncertainty, providing alternative scenarios or interpretations, and emphasizing the implications of uncertainty. Acknowledging uncertainty upfront demonstrates honesty and awareness of data limitations, while quantifying uncertainty using statistical measures like confidence intervals or error bars provides a concrete understanding of the range of possible values.

Effectively managing uncertainty in analyses is vital for businesses, necessitating a transparent approach to address inherent challenges. Dismissing uncertainty can erode trust, emphasizing the need for openness. Businesses should communicate the limitations & assumptions underlying their analyses, providing stakeholders with a clear understanding of how these factors influence conclusions. For instance, a company may transparently state, "This analysis is grounded in a subset of our customer base, recognizing its potential divergence from the broader population. Consequently, results are subject to a margin of error, ensuring a comprehensive understanding of presented insights." Such forthrightness strengthens credibility amid uncertainty

Line graphs gives the illusion of absolute certainty for each value on the x-axis. Consider adding error bands to quantify the level of uncertainty effectively.

In the pharmaceutical industry, navigating uncertainties is crucial when developing a new drug. Acknowledging the variability in clinical trial outcomes, a company might transparently communicate the efficacy results with associated confidence intervals. For instance, instead of asserting a fixed improvement rate, expressing it as a range (e.g., 15% improvement with a confidence interval of 12% to 18%) conveys the statistical confidence in the findings. This approach enables stakeholders, from regulatory bodies to investors, to make informed decisions considering the inherent uncertainties in drug development.

Some nuanced tips for effectively quantifying uncertainty: 1. Choose appropriate measures for quantifying uncertainty based on the type of data and analysis. E.g. confidence intervals are suitable for estimating the range of true values from a sample, while error bars represent the variability in a single measurement. 2. Interpret and understand the meaning and implications of the uncertainty measures you employ. E.g. a confidence interval of 95% indicates that you are 95% confident that the true value lies within the specified range. 3. Consider conducting a sensitivity analysis to assess the robustness of your findings and uncover potential sources of uncertainty.

One way to diminish an uncertainty of your conclusions is to add the methodology behind any conclusions. Moreover, a few words of the rationale behind research method might prove vital. Experts on the topic could discuss it further and even provide further evidence of the conclusions reliability. Also, any reader of your work would be able to more easily grasp any difficult concept.

Business process models are the best ways to visualise the whole workflow, uncertainties being clear in the flow highlights every stakeholders intention to take steps to resolve it.

o effectively communicate uncertainties in project timelines to stakeholders, utilizing clear and tailored visualizations becomes paramount. Employing a Gantt chart with shaded regions depicting the expected duration and potential delays can offer a concise yet comprehensive overview. Additionally, a risk matrix could visually represent the probability and impact of various project uncertainties. Striking the right balance in visual complexity ensures that stakeholders grasp the nuances of project timelines without feeling overwhelmed or misled, fostering informed decision-making and proactive risk management.

Some tips: 1. Visualisations should be easy to interpret and follow. Use clear labels, consistent formatting, and adequate spacing to avoid clutter and confusion. 2. Visual elements, e.g. colour gradients, shading, or annotations, draw attention to important patterns or trends in the data, particularly those related to uncertainty. 3. Interactive visualisations provide a more dynamic and engaging way to explore uncertainty, allowing users to directly manipulate data and observe the corresponding changes in uncertainty measures. 4. Be mindful of potential misinterpretations that arise from the choice of visualisation. Use clear legends, avoid complex patterns, and ensure that the scale and range of the visualisation are appropriate.

A cheat code in research and analyst call work is to provide the same information several times but expressed differently. A table which structures it in a certain way or a graph/chart with color coding proves invaluable. Our brain works best if provoked with information in pieces, and repeated several times through different means. Never underestimate the power of the visual.

One thing I have found helpful is using PowerBI or Excel dashboard to visulaise data. An analyst can always update the dataset as per latest trend, expert opinion or bring a whole new relation in a single board! However, keeping that dashbroad neat is the key to better understanding.

Simplifying the communication of uncertainty is vital for ensuring diverse stakeholders can engage meaningfully with complex analyses. Balancing clarity and precision is crucial. Using plain and concise language allows for key insights without overwhelming with technical details. It's essential to convey the nuanced nature of uncertainties accurately, avoiding oversimplification that may lead to misunderstandings. For instance, presenting findings with a focus on actionable steps, like improving service quality and customer retention, facilitates accessible understanding while maintaining analysis integrity, fostering informed decision-making.

This depends on the opposite party we are communicating with. For exemple: If a Credit Analyst is presenting loan portfolio to HoCRM about hightened NPI, it needs no simplification. Simplification in here will be a waste of time and unnecessary. On the contrary, if the analyst is presenting to stakeholders same term needs to simplified as "the amount that has not been repaid in due time" has increased. This means we need to focus on collection.

I am not a huge fan of excessive caveating/qualifying the observations as it dilutes the impact of the insights. Nevertheless, some suggested to qualify uncertainty. USE WITH CAUTION!: 1. Instead of "growth", say "directional uplift" 2. Instead of "decline", say "dip" or "downward trend" 3. Use "snapshot" to emphasize the measurements are of that moment in time and should not be generalised to longer time periods. 4. Use "flash estimates" to downplay the robustness of the measurements. 5. Use qualifying words like "suggest", "indicative", "connote", "point towards", "imply"

In essence, qualifying uncertainty is not about diminishing the value of data analysis but rather about enhancing its reliability and usefulness. It is important because: 1. It allows decision-makers to consider the limitations and risks involved in their choices, leading to more prudent and well-informed actions. 2. It highlights areas where more data, more robust methods, or more rigorous analysis may be needed to reduce uncertainty and strengthen conclusions. 3. It encourages open discussion about limitations, assumptions, and alternative perspectives, leading to a more collective understanding of the data and its implications.

The best way to address the uncertainty is to address it as an opportunity for the next R&D phase. After all, uncertainties often pop out due to your analytical input. If you can link the two, your analysis becomes a valuable asset for progress instead of "failure."

I guess one can take examples from live-situations. One anecdote that I remember from my own event in New York is as following: After reaching NYC with a friend I got ditched and was asked to spend the rest of the day on my own. I was uncertain about what to do or who to contact, but there was hope. I came up with a strategy to visit the Times Square thinking as it's a hotspot in NYC, I might just find someone known to me there. Fortunately, I found not one but three of my friends in Times Square that evening. So there must be a factor of determination/hope in dealing with uncertainty and also an ability to confront uncertain data with positive outlook, perhaps more life-like situations can help deal with uncertainty in data analytics!

This particular step can open many new dimensions to tackle any uncertainty. Audiance often has numerous questions or ideas which they are reluctant to share. But inviting them in to join in analysis makes them feel valued. Sometimes it leads to new opportunities, collaboration or even breakthrough idea generation!

When communicating uncertainty analysis results, be transparent about the limitations of the analysis, use clear language, and provide context for non-experts. Present the results with a range or confidence interval to convey the uncertainty. Acknowledge potential sources of error and explain the methodology used. This openness helps maintain credibility by demonstrating a thorough understanding of the uncertainties involved.

Before starting the communication with audiance it is better to create a common understanding of decision problems. So that, everyone can relate key findings with the issue. It makes the ausiance understand, relate and solve better.