How Not to Mislead with Your Data-Driven Story

is all over the place. There are numerous books, articles, tutorials, and movies, a few of which I’ve written or created.

In my expertise, most of those sources are likely to current knowledge storytelling in an overwhelmingly optimistic gentle. However these days, one concern has been on my thoughts:

What if our tales, as a substitute of clarifying, mislead?

The picture above exhibits one of many condo buildings in my neighborhood. Now, check out the photograph on the left and picture one of many residences within the white constructing is up on the market. You might be contemplating shopping for it. You’ll probably deal with the instant environment, particularly as introduced within the vendor’s photographs. Discover something uncommon? In all probability not, at the least not instantly.

Ought to the instant setting be a dealbreaker? For my part, not essentially. It’s not probably the most picturesque or charming spot—only a typical block in a median neighborhood in Warsaw. Or is it?

Let’s take a brief stroll round to the again of the constructing. And… shock: there’s a public toilet proper there. Nonetheless be ok with the situation? Perhaps sure, possibly no. One factor is evident: you’d need to know {that a} public rest room sits slightly below your future balcony.

Moreover, the condo is positioned within the decrease a part of the constructing, whereas the remainder of the towers rise above it. That is one other issue that could be important. Each these “points” for certain may be introduced up in value negotiations.

This straightforward instance illustrates how simply tales (on this case, utilizing photographs) may be misinterpreted. From one angle, all the things appears high quality, even inviting. Take just a few steps to the fitting, and… whoops.

The identical scenario can occur in our “skilled” lives. What if audiences, satisfied they’re making knowledgeable, data-backed choices, are being subtly steered within the unsuitable path—not by false knowledge, however by the way in which it’s introduced?

This submit builds on an article I wrote in 2024 about deceptive visualizations [1]. Right here, I need to take a bit broader perspective, exploring how the construction and circulation of a narrative itself can unintentionally (or intentionally) lead folks to incorrect conclusions, and the way we will keep away from that.

Knowledge storytelling is subjective

We regularly prefer to imagine that “knowledge speaks for itself.” However in actuality, it not often does. Each chart, dashboard, or headline constructed round a dataset is formed by human selections:

• what to incorporate,
• what to go away out,
• how you can body the message?

This highlights a core problem of data-driven storytelling: it’s inherently subjective. That subjectivity comes from the discretion now we have in proving the purpose we need to make:

• selecting which knowledge to current,
• choosing applicable evaluation approach,
• deciding on arguments to emphasize,
• and even what to to make use of.

Subjectivity additionally lies in interpretation — each ours and our viewers’s — and of their willingness to behave on the data. This opens the door to biases. If we aren’t cautious, we will simply cross the road from subjectivity into unethical storytelling.

This text examines the hidden biases embedded in knowledge storytelling and the way we will transition from manipulation to significant insights.

We want tales

Subjective or not, we’d like tales. Tales are important to us as a result of they assist make sense of the world. They carry our values, protect our historical past, and spark our creativeness. By way of tales, we join with others, study from previous experiences, and discover what it means to be human. Regardless of your nationality, tradition, or faith, now we have all heard numerous tales which have formed us. Informed us by our grandparents, mother and father, lecturers, pals, and colleagues at work. Tales evoke emotion, encourage motion, and form our id, each individually and collectively. In each tradition and throughout all ages, storytelling has been a robust technique of understanding life, sharing data, and constructing group.

However whereas tales can enlighten, they will additionally mislead. A compelling narrative has the ability to form notion, even when it distorts details or oversimplifies advanced points. Tales usually depend on emotion, selective element, and a transparent message, which might make them persuasive, but additionally dangerously reductive. When used carelessly or manipulatively, storytelling can reinforce biases, obscure fact, or drive choices based mostly extra on feeling than motive.

Within the subsequent a part of this text, I’ll discover the potential issues with tales — particularly in data-driven contexts — and the way their energy can unintentionally (or deliberately) misguide our understanding.

Narrative biases in data-driven storytelling

Bias 1. Knowledge is much, far-off from interpretation

Right here’s an instance of a visible from a report titled “Kentucky Juvenile Justice Reform Evaluation: Assessing the Effects of SB 200 on Youth Dispositional Outcomes and Racial and Ethnic Disparities.”

The graph exhibits that younger offenders in Kentucky are much less more likely to reoffend if, after their first offense, they’re routed by a diversion program. This program connects them with group assist, reminiscent of social staff and therapists, to handle deeper life challenges. That’s a robust narrative with real-world implications: it helps lowering our reliance on an costly legal justice system, justifies elevated funding for non-profits, and factors towards significant methods to enhance lives.

However right here’s the issue: except you have already got sturdy knowledge literacy and topic data, these conclusions will not be instantly apparent from the graph. Whereas the report does make this level, it doesn’t accomplish that till practically 20 pages later. This can be a traditional instance of how the construction of educational reporting can mute the story’s influence. It outcomes from the truth that knowledge is introduced visually in a single part and interpreted textually in several (and generally distant) sections of the doc.

Bias 2. The Story of the Lacking Map: Choice Bias

Selecting which knowledge factors (cherries 😊) to incorporate (and which to disregard) is among the strongest — and sometimes most missed — acts of bias. And maybe no trade illustrated this higher than Huge Tobacco.

The now-famous abstract of their authorized technique says all of it:

Sure, smoking causes lung most cancers, however not in individuals who sue us.

That quote completely captures the tone of tobacco litigation within the late twentieth century, the place firms confronted a wave of lawsuits from clients affected by ailments linked to smoking. Regardless of overwhelming medical and scientific consensus, tobacco companies routinely deflected accountability utilizing a collection of arguments that, whereas generally legally strategic, have been scientifically absurd.

Listed below are 4 of probably the most egregious cherry-picking techniques they utilized in courtroom, based mostly on this text [2].

Cherry-pick tactic 1: use “exception fallacy” tactic in authorized or rhetorical contexts.

Sure, smoking causes most cancers — however not this one.

• The plaintiff had a uncommon type of most cancers, like bronchioloalveolar carcinoma (BAC) or mucoepidermoid carcinoma, which they claimed weren’t conclusively linked to smoking.
• In a single case, they argued the most cancers was from the thymus, not the lungs, regardless of overwhelming medical proof.

Cherry-pick tactic 2: Spotlight obscure exceptions or uncommon most cancers sorts to problem normal epidemiological proof.

It wasn’t our model.

• “Certain, tobacco might have brought on the illness — however not our cigarettes.”
• In Ierardi v. Lorillard, the corporate argued that the plaintiff’s publicity to asbestos-laced cigarette filters (Micronite) occurred outdoors the slender 4-year window after they have been used, regardless that 585 million packs have been bought throughout that point.

Cherry-pick tactic 3: Deal with model or product variation as a approach to shift blame.

In a number of instances, reminiscent of Ierardi v. Lorillard and Lacy v. Lorillard, the protection admitted that cigarettes could cause most cancers however argued that the plaintiff:

• Didn’t use their model on the time of publicity,
• Or didn’t use the particular model of the product that was most harmful (e.g., Kent cigarettes with the asbestos-containing Micronite filter),
• Or didn’t use the particular model of the product that was most harmful (e.g., Kent cigarettes with the asbestos-containing Micronite filter),
• window years in the past, making it unlikely the plaintiff was uncovered.

This tactic shifts the narrative from

Our product brought on hurt.

to

Perhaps smoking brought on hurt—however not ours.

Cherry-pick tactic 4: Emphasize each different doable threat issue — no matter plausibility — to deflect from tobacco’s position.

There have been different threat elements.

• In lots of lawsuits, firms pointed to various causes of sickness: asbestos, diesel fumes, alcohol, genetics, weight loss program, weight problems, and even spicy meals.
• In Allgood v. RJ Reynolds, the protection blamed the plaintiff’s situation partly on his fondness for “Tex-Mex meals.”

Cherry-picking isn’t all the time apparent. It may well cover in authorized defenses, advertising copy, dashboards, and even tutorial stories. However when solely the information that serves the story will get informed, it stops being perception and begins turning into manipulation.

Bias 3: The Mirror within the Forest: How the Similar Knowledge Tells Totally different Tales

How we phrase outcomes can skew interpretation. Ought to we are saying “Unemployment drops to 4.9%” or “Tens of millions nonetheless jobless regardless of beneficial properties”? Each may be correct. The distinction lies in emotional framing.

In essence, framing is a strategic storytelling approach that may considerably influence how a narrative is obtained, understood, and remembered. By understanding the ability of framing, storytellers can craft narratives that resonate deeply with their viewers and obtain their desired targets. I current some examples in Desk 1.

Bias 4: “The Dragon of Design: How Magnificence Beguiles the Fact”

Visuals simplify knowledge, however they will additionally manipulate notion. In my older article [1], I listed 14 misleading visualization techniques. Here’s a abstract of them.

1. Utilizing the unsuitable chart sort: Selecting charts that confuse moderately than make clear — like 3D pie charts or inappropriate comparisons — makes it tougher to see the story the information tells.
2. Including distracting components: Stuffing visuals with logos, decorations, darkish gridlines, or litter hides the necessary insights behind noise and visible overload.
3. Overusing colours: Utilizing too many colours can distract from the main focus. And not using a clear coloration hierarchy, nothing stands out, and the viewer is overwhelmed.
4. Random knowledge ordering: Scrambling classes or time collection knowledge obscures patterns and prevents clear comparisons.
5. Manipulating axis scales: Truncating the y-axis exaggerates variations. Extending it minimizes significant variation. Each distort notion.
6. Creating development illusions: Utilizing inconsistent time frames, selective knowledge factors, or poorly spaced axes to make non-trends look important.
7. Cherry-picking knowledge: Solely displaying the components of the information that assist your level, ignoring the complete story or contradicting proof.
8. Omitting visible cues: Eradicating labels, legends, gridlines, or axis scales to make knowledge laborious to interpret, or laborious to problem.
9. Overloading charts: Packing an excessive amount of knowledge into one chart may be distracting and complicated, particularly when important knowledge is buried in visible chaos.
10. Displaying solely cumulative values: Utilizing cumulative plots to suggest easy progress whereas hiding volatility or declines in particular person intervals.
11. Utilizing 3D results: 3D charts skew notion and make comparisons tougher, usually resulting in deceptive details about measurement or proportion.
12. Making use of gradients and shading: Fancy textures or gradients shift focus and add visible weight to areas that may not deserve it.
13. Deceptive or obscure titles: A impartial or technical title can downplay the urgency of findings. A dramatic one can exaggerate a minor change.
14. Utilizing junk charts: Visually overdesigned, advanced, or overly creative charts which are laborious to interpret and simple to misinterpret.

Bias 5: “The Story-Spinning Machine: However Who Holds the Thread?”

Fashionable instruments like Energy BI Copilot or Tableau Pulse are more and more producing summaries and “insights” in your behalf. To not point out crafting summaries, narratives, or complete shows ready by LLMs like ChatGPT or Gemini.

However right here’s the catch:
These instruments are skilled on patterns, not ethics.

AI can’t inform when it’s making a deceptive story. In case your immediate or dataset is biased, the output will probably be biased as properly, and at a machine scale.

This raises a important query: Are we utilizing AI to democratize perception, or to mass-produce narrative spin?

A current BBC investigation discovered that main AI chatbots ceaselessly distort or misrepresent present occasions, even when utilizing BBC articles as their supply. Over half of the examined responses contained important points, together with outdated details, fabricated or altered quotes, and confusion between opinion and reporting. Examples ranged from incorrectly stating that Rishi Sunak was nonetheless the UK prime minister to omitting key authorized context in high-profile legal instances. BBC executives warned that these inaccuracies threaten public belief in information and urged AI firms to collaborate with publishers to enhance transparency and accountability.[3]

Feeling overwhelmed? You’ve solely seen the start. Knowledge storytelling can fall prey to quite a few cognitive biases, every subtly distorting the narrative.

Take affirmation bias, the place the storyteller highlights solely knowledge that helps their assumptions—proclaiming, “Our marketing campaign was a hit!”—whereas ignoring contradictory proof. Then there’s end result bias, which credit success to sound technique: “We launched the product and it thrived, so our method was excellent,”—even when luck performed a serious position.

Survivorship bias focuses solely on the winners—startups that scaled or campaigns that went viral—whereas neglecting the numerous that failed utilizing the identical strategies. Narrative bias oversimplifies complexity, shaping messy realities into tidy conclusions, reminiscent of “Vaping is all the time safer,” with out adequate context.

Anchoring bias causes folks to fixate on the primary quantity introduced—like a 20% forecast—distorting how subsequent info is interpreted. Omission bias arises when necessary knowledge is unnoticed, as an illustration, solely highlighting top-performing areas whereas ignoring underperforming ones.

Projection bias assumes that others interpret knowledge the identical method the analyst does: “This dashboard speaks for itself,”—but it could not, particularly for stakeholders unfamiliar with the context. Scale bias misleads with disproportionate framing—“A 300% improve!” sounds spectacular till you study it went from only one to a few customers.

Lastly, causality bias attracts unfounded conclusions from correlations: “Customers stayed longer after we added popups—they have to love them!”—with out testing whether or not popups have been the precise trigger.

How you can “Unbias” Knowledge Storytelling

Each knowledge story is a alternative. In a world the place consideration spans are quick and AI writes quicker than people, these selections are extra highly effective — and harmful — than ever.

As knowledge scientists, analysts, and storytellers, we should method narrative selections with the identical stage of rigor and thoughtfulness that we apply to statistical fashions. Crafting a narrative from knowledge is not only about readability or engagement—it’s about accountability. Each alternative we make in framing, emphasis, and interpretation shapes how others understand the reality. And on the finish of the day, probably the most harmful tales will not be the false ones—they’re those that really feel like details.

On this a part of the article, I’ll share a number of sensible methods that will help you strengthen your knowledge storytelling. These concepts will deal with how you can be each compelling and credible—how you can craft narratives that have interaction your viewers with out oversimplifying or deceptive them. As a result of when executed properly, knowledge storytelling doesn’t simply talk perception—it builds belief.

Technique 1: The Smart Wizard’s Rule: Ask, Don’t Enchant

On the planet of information and evaluation, probably the most insightful storytellers don’t announce their conclusions with dramatic aptitude—they lead with considerate questions. As an alternative of presenting daring declarations, they invite reflection by asking, “What do you see?” This method encourages others to find insights on their very own, fostering understanding moderately than passive acceptance.

Take into account a graph displaying a decline in take a look at scores. A surface-level interpretation would possibly instantly declare, “Our faculties are failing,” sparking concern or blame. However a extra cautious, analytical response could be, “What elements might clarify this modification? Might it’s a brand new testing format, adjustments in pupil demographics, or one thing else?” Equally, when gross sales rise following the launch of a brand new characteristic, it’s tempting to attribute the rise solely to the characteristic. But a extra rigorous method would ask, “What different variables modified throughout this era?”

By main with questions, we create house for interpretation, dialogue, and deeper considering. This methodology guards towards false certainty and encourages a extra collaborative, considerate exploration of information. A robust narrative ought to information the viewers, moderately than forcing them towards a predetermined conclusion.

Technique 2: The Mirror of Many Truths: Supply Counter-Narratives

Good knowledge storytelling doesn’t cease at a single interpretation. Complicated datasets usually permit for a number of legitimate views, and it’s the storyteller’s accountability to acknowledge them. Presenting a counter-narrative—“right here’s one other method to have a look at this”—invitations important considering and builds credibility.

For instance, a chart might present that coronary heart illness charges are declining general. That looks as if a hit. However a better look might reveal that the advance is concentrated in higher-income areas, whereas charges in rural or underserved communities stay excessive. Presenting each views—progress and disparity—supplies a extra complete and sincere image of the problem.

By providing counter-narratives, we guard towards oversimplification and assist our viewers perceive the nuance behind the numbers.

Technique 3: The Curse of Crooked Charts: Keep away from Misleading Visuals

Visuals are highly effective, however that energy should be used responsibly. Deceptive charts can distort notion by delicate tips, reminiscent of truncated axes that exaggerate variations, unlabeled models that obscure the size, or ornamental litter that distracts from the message. To keep away from these pitfalls, all the time clearly label axes, begin scales from zero when applicable, and select chart sorts that greatest match the information, not simply their aesthetic attraction. Deception doesn’t all the time come from malice—generally it’s simply careless design. However both method, it erodes belief. A clear, sincere visible is much extra persuasive than a flashy one which hides the small print.

Take, for instance, the 2 charts proven in Picture 7. The one on the left is cluttered and laborious to interpret. Its title is obscure, the extreme use of coloration is distracting, and pointless components—like heavy borders, gridlines, and shading—solely add to the confusion. There are not any visible cues to information the viewer, leaving the viewers to guess what the creator is making an attempt to say.

In distinction, the chart on the fitting is much more practical. It strips away the noise, utilizing simply three colours: gray for context, blue to spotlight key info, and a clear white background. Most significantly, the title conveys the principle message, permitting the viewers to understand the purpose at a look.

Technique 4: Communicate Actually of Shadows: The Knowledge of Embracing Uncertainty

Uncertainty is an inherent a part of working with knowledge, and acknowledging it doesn’t weaken your story—it strengthens your credibility. Transparency round uncertainty is a trademark of accountable knowledge communication. Once you talk components like confidence intervals, margins of error, or the assumptions behind a mannequin, you’re not simply being technically correct—you’re demonstrating honesty and humility. It exhibits that you simply respect your viewers’s potential to interact with complexity, moderately than oversimplifying to take care of a clear narrative.

Uncertainty can come up from numerous sources, together with restricted pattern sizes, noisy or incomplete knowledge, altering circumstances, or the assumptions inherent in predictive fashions. As an alternative of ignoring or smoothing over these limitations, good storytellers carry them to the forefront—visually and verbally. Doing so encourages important considering and opens the door for dialogue. It additionally protects your work from misinterpretation, misuse, or overconfidence in outcomes. Briefly, by being open about what the information can’t inform us, we give extra weight to what it may well. Beneath, I current a number of examples of how you can embody info on uncertainty in your knowledge story.

1. Replace on confidence intervals
   As an alternative of: “Income will develop by 15% subsequent quarter.”
   Use: “We mission a 15% progress, with a 95% confidence interval of 12%–18%.”
2. Depart a margin of error.
   As an alternative of: “Buyer satisfaction is at 82%.”
   Use: “Buyer satisfaction is 82%, ±3% margin of error.”
3. Lacking knowledge indicators
   Use visible cues, reminiscent of pale bars, dashed traces, or shaded areas, on charts to point gaps.
   Add footnotes: “Knowledge for Q2 is incomplete as a result of reporting delays.”
4. Mannequin assumptions
   Instance: “This forecast assumes no important change in person conduct or market circumstances.”
5. A number of eventualities
   Current best-case, worst-case, and most-likely eventualities to mirror a variety of doable outcomes.
6. Probabilistic language
   As an alternative of: “It will occur.”
   Use: “There’s a 70% likelihood this end result happens below present circumstances.”
7. Knowledge high quality notes
   Spotlight points like small pattern sizes or self-reported knowledge:
   “Outcomes are based mostly on a survey of 100 respondents and should not mirror the broader inhabitants.”
8. Error bars on charts
   Visually present uncertainty by together with error bars or shaded confidence bands in graphs.
9. Transparency in limitations
   Instance: “This evaluation doesn’t account for seasonal variation or exterior financial elements.”
10. Qualitative clarification
    Use captions or callouts in shows or dashboards:
    “Knowledge tendencies are indicative, however additional validation is required.”

You would possibly surprise, “However gained’t highlighting these uncertainties weaken my story or make me appear uncertain of the outcomes?” Quite the opposite, acknowledging uncertainty doesn’t sign a insecurity; it exhibits depth, professionalism, and integrity. It conveys to your viewers that you simply perceive the complexity of the information and will not be making an attempt to oversell a simplistic conclusion. Sharing what you do know, alongside what you don’t, creates a extra balanced and credible narrative. Individuals are much more more likely to belief your insights after they see that you simply’re being sincere in regards to the limitations. It’s not about dampening your story—it’s about grounding it in actuality.

Technique 5: Reveal the Roots of the Story: Let Fact Journey with Its Sources

Each story wants roots, and on the planet of information storytelling, these roots are your sources. A fantastic chart or putting quantity means little in case your viewers can’t see the place it got here from. Was it a randomized survey? Administrative knowledge? Social media scraping? Similar to a traveler trusts a information who is aware of the trail, readers usually tend to belief your insights after they can hint them again to their origins. Transparency about knowledge sources, assortment strategies, assumptions, and even limitations will not be an indication of weak spot—it’s a mark of integrity. Once we reveal the roots of the story, we give our story depth, credibility, and resilience. Knowledgeable choices can solely develop in well-tended soil.

Closing remarks

Knowledge-driven storytelling is each an artwork and a accountability. It offers us the ability to make info significant—but additionally the ability to mislead, even unintentionally. On this article, we’ve explored a forest of biases, design traps, and narrative temptations that may subtly form notion and deform the reality. Whether or not you’re an information scientist, communicator, or decision-maker, your tales carry weight—not only for what they present, however for the way they’re informed.

So allow us to inform tales that illuminate, not obscure. Allow us to lead with questions, not conclusions. Allow us to reveal uncertainty, not cover behind false readability. And above all, allow us to anchor our insights in clear sources and humble interpretation. The aim isn’t perfection—it’s integrity. As a result of in a world full of noise and narrative spin, probably the most highly effective story you possibly can inform is one which’s each clear and sincere.

In the long run, storytelling will not be about controlling the message—it’s about incomes belief. And belief, as soon as misplaced, will not be simply gained again. So select your tales rigorously. Form them with care. And keep in mind: the reality might not all the time be flashy, however it all the time finds its approach to the sunshine.

And another factor: in case you’ve ever noticed (or unintentionally created) a biased knowledge story, share your expertise within the feedback. The extra we floor these narratives, the higher all of us get at telling knowledge truths, not simply knowledge tales.

References

[1] How not to Cheat with Data Visualizations, Michal Szudejko, In direction of Knowledge Science

[2] Tobacco manufacturers’ defence against plaintiffs’ claims of cancer causation: throwing mud at the wall and hoping some of it will stick, A number of Authors, Nationwide Library of Drugs

[3] AI chatbots distort and mislead when asked about current affairs, BBC finds, Matthew Weaver

Disclaimer

This submit was initially written utilizing Microsoft Phrase, and the spelling and grammar have been checked with Grammarly. I reviewed and adjusted any modifications to make sure that my meant message was precisely mirrored. All different makes use of of AI (as an illustration picture and pattern knowledge technology) have been disclosed immediately within the textual content.