How To not Mislead with Your Knowledge-Pushed Story

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

In my expertise, most of those assets are inclined to current information storytelling in an overwhelmingly optimistic gentle. However recently, one concern has been on my thoughts:

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

The picture above reveals one of many residence buildings in my neighborhood. Now, check out the photograph on the left and picture one of many flats within the white constructing is up on the market. You might be contemplating shopping for it. You’ll possible give attention to the fast environment, particularly as introduced within the vendor’s pictures. Discover something uncommon? Most likely not, not less than not instantly.

Ought to the fast 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? Possibly sure, possibly no. One factor is obvious: you’ll wish to know {that a} public bathroom sits just under your future balcony.

Moreover, the residence 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 will be introduced up in value negotiations.

This straightforward instance illustrates how simply tales (on this case, utilizing pictures) will be misinterpreted. From one angle, all the things seems to be advantageous, even inviting. Take a couple of steps to the suitable, and… whoops.

The identical state of affairs can occur in our “skilled” lives. What if audiences, satisfied they’re making knowledgeable, data-backed selections, are being subtly steered within the improper course—not by false information, however by the best way it’s introduced?

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

Knowledge storytelling is subjective

We frequently prefer to consider that “information 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,
• find out how to 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 wish to make:

• selecting which information to current,
• deciding on acceptable evaluation method,
• deciding on arguments to stress,
• 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’re not cautious, we will simply cross the road from subjectivity into unethical storytelling.

This text examines the hidden biases embedded in information 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 means of tales, we join with others, be taught from previous experiences, and discover what it means to be human. Irrespective of your nationality, tradition, or faith, now we have all heard numerous tales which have formed us. Informed us by our grandparents, dad and mom, lecturers, pals, and colleagues at work. Tales evoke emotion, encourage motion, and form our identification, each individually and collectively. In each tradition and throughout all ages, storytelling has been a robust technique of understanding life, sharing data, and constructing neighborhood.

However whereas tales can enlighten, they’ll additionally mislead. A compelling narrative has the ability to form notion, even when it distorts info or oversimplifies advanced points. Tales typically depend on emotion, selective element, and a transparent message, which might make them persuasive, but in addition dangerously reductive. When used carelessly or manipulatively, storytelling can reinforce biases, obscure reality, or drive selections primarily based 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 way, distant from interpretation

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

The graph reveals that younger offenders in Kentucky are much less more likely to reoffend if, after their first offense, they’re routed via a diversion program. This program connects them with neighborhood help, equivalent to 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 felony 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 robust information literacy and topic data, these conclusions are usually not instantly apparent from the graph. Whereas the report does make this level, it doesn’t achieve this till almost 20 pages later. This can be a traditional instance of how the construction of educational reporting can mute the story’s affect. It outcomes from the truth that information is introduced visually in a single part and interpreted textually in several (and typically distant) sections of the doc.

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

Selecting which information factors (cherries 😊) to incorporate (and which to disregard) is among the strongest — and sometimes most neglected — acts of bias. And maybe no trade illustrated this higher than Large 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 prospects affected by illnesses linked to smoking. Regardless of overwhelming medical and scientific consensus, tobacco corporations routinely deflected duty utilizing a collection of arguments that, whereas typically legally strategic, have been scientifically absurd.

Listed below are 4 of probably the most egregious cherry-picking techniques they utilized in court docket, primarily based 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 basic epidemiological proof.

It wasn’t our model.

• “Certain, tobacco might have brought about 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, despite the fact that 585 million packs have been bought throughout that point.

Cherry-pick tactic 3: Concentrate on model or product variation as a technique to shift blame.

In a number of circumstances, equivalent to 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 precise model of the product that was most harmful (e.g., Kent cigarettes with the asbestos-containing Micronite filter),
• Or didn’t use the precise 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 about hurt.

to

Possibly smoking brought about hurt—however not ours.

Cherry-pick tactic 4: Emphasize each different attainable danger issue — no matter plausibility — to deflect from tobacco’s function.

There have been different danger elements.

• In lots of lawsuits, firms pointed to various causes of sickness: asbestos, diesel fumes, alcohol, genetics, eating regimen, 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 could actually conceal in authorized defenses, advertising copy, dashboards, and even educational stories. However when solely the information that serves the story will get informed, it stops being perception and begins changing 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 “Hundreds of thousands nonetheless jobless regardless of positive factors”? Each will be correct. The distinction lies in emotional framing.

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

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

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

1. Utilizing the improper chart kind: Selecting charts that confuse fairly than make clear — like 3D pie charts or inappropriate comparisons — makes it more durable to see the story the information tells.
2. Including distracting parts: Stuffing visuals with logos, decorations, darkish gridlines, or litter hides the vital insights behind noise and visible overload.
3. Overusing colours: Utilizing too many colours can distract from the main focus. With no clear colour hierarchy, nothing stands out, and the viewer is overwhelmed.
4. Random information ordering: Scrambling classes or time collection information 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 pattern illusions: Utilizing inconsistent time frames, selective information factors, or poorly spaced axes to make non-trends look important.
7. Cherry-picking information: Solely displaying the elements of the information that help your level, ignoring the total story or contradicting proof.
8. Omitting visible cues: Eradicating labels, legends, gridlines, or axis scales to make information arduous to interpret, or arduous to problem.
9. Overloading charts: Packing an excessive amount of information into one chart will be distracting and complicated, particularly when essential information is buried in visible chaos.
10. Displaying solely cumulative values: Utilizing cumulative plots to suggest clean progress whereas hiding volatility or declines in particular person durations.
11. Utilizing 3D results: 3D charts skew notion and make comparisons harder, typically 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 which may not deserve it.
13. Deceptive or imprecise 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 inventive charts which can be arduous to interpret and straightforward to misinterpret.

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

Trendy 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 possible be biased as properly, and at a machine scale.

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

A latest 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 info, 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 felony circumstances. 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 information that helps their assumptions—proclaiming, “Our marketing campaign was successful!”—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 strategy was excellent,”—even when luck performed a significant function.

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

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

Projection bias assumes that others interpret information 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 be taught 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.

Methods to “Unbias” Knowledge Storytelling

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

As information scientists, analysts, and storytellers, we should strategy narrative selections with the identical degree of rigor and thoughtfulness that we apply to statistical fashions. Crafting a narrative from information isn’t just about readability or engagement—it’s about duty. 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 are usually not the false ones—they’re those that really feel like info.

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

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

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

Think about a graph displaying a decline in take a look at scores. A surface-level interpretation may instantly declare, “Our colleges are failing,” sparking concern or blame. However a extra cautious, analytical response can be, “What elements may clarify this transformation? Might it’s a brand new testing format, adjustments in scholar demographics, or one thing else?” Equally, when gross sales rise following the launch of a brand new function, it’s tempting to attribute the rise solely to the function. But a extra rigorous strategy would ask, “What different variables modified throughout this era?”

By main with questions, we create area for interpretation, dialogue, and deeper considering. This technique guards in opposition to false certainty and encourages a extra collaborative, considerate exploration of information. A powerful narrative ought to information the viewers, fairly than forcing them towards a predetermined conclusion.

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

Good information storytelling doesn’t cease at a single interpretation. Complicated datasets typically enable for a number of legitimate views, and it’s the storyteller’s duty to acknowledge them. Presenting a counter-narrative—“right here’s one other method to take a look at this”—invitations essential considering and builds credibility.

For instance, a chart might present that coronary heart illness charges are declining general. That looks like successful. However a more in-depth 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—offers a extra complete and trustworthy image of the difficulty.

By providing counter-narratives, we guard in opposition to 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 have to be used responsibly. Deceptive charts can distort notion via delicate methods, equivalent to truncated axes that exaggerate variations, unlabeled models that obscure the dimensions, 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 acceptable, and select chart sorts that finest match the information, not simply their aesthetic attraction. Deception doesn’t all the time come from malice—typically it’s simply careless design. However both method, it erodes belief. A clear, trustworthy visible is way 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 arduous to interpret. Its title is imprecise, the extreme use of colour is distracting, and pointless parts—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 writer is making an attempt to say.

In distinction, the chart on the suitable is way 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: Converse Actually of Shadows: The Knowledge of Embracing Uncertainty

Uncertainty is an inherent a part of working with information, and acknowledging it doesn’t weaken your story—it strengthens your credibility. Transparency round uncertainty is a trademark of accountable information communication. Whenever you talk parts 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 reveals that you simply respect your viewers’s means to have interaction with complexity, fairly than oversimplifying to keep up a clear narrative.

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

1. Replace on confidence intervals
   As a substitute of: “Income will develop by 15% subsequent quarter.”
   Use: “We challenge a 15% development, with a 95% confidence interval of 12%–18%.”
2. Go away a margin of error.
   As a substitute of: “Buyer satisfaction is at 82%.”
   Use: “Buyer satisfaction is 82%, ±3% margin of error.”
3. Lacking information indicators
   Use visible cues, equivalent to pale bars, dashed strains, 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 consumer habits or market circumstances.”
5. A number of situations
   Current best-case, worst-case, and most-likely situations to mirror a variety of attainable outcomes.
6. Probabilistic language
   As a substitute of: “This can occur.”
   Use: “There’s a 70% probability this end result happens underneath present circumstances.”
7. Knowledge high quality notes
   Spotlight points like small pattern sizes or self-reported information:
   “Outcomes are primarily based on a survey of 100 respondents and will 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 traits are indicative, however additional validation is required.”

You may surprise, “However received’t highlighting these uncertainties weaken my story or make me appear not sure of the outcomes?” Quite the opposite, acknowledging uncertainty doesn’t sign a insecurity; it reveals depth, professionalism, and integrity. It conveys to your viewers that you simply perceive the complexity of the information and are usually not 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 trustworthy 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. An exquisite chart or placing quantity means little in case your viewers can’t see the place it got here from. Was it a randomized survey? Administrative information? 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 information sources, assortment strategies, assumptions, and even limitations will not be an indication of weak point—it’s a mark of integrity. Once we reveal the roots of the story, we give our story depth, credibility, and resilience. Knowledgeable selections can solely develop in well-tended soil.

Closing remarks

Knowledge-driven storytelling is each an artwork and a duty. It provides us the ability to make info significant—but in addition 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 a 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 conceal 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 stuffed with noise and narrative spin, probably the most highly effective story you possibly can inform is one which’s each clear and trustworthy.

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 received again. So select your tales fastidiously. Form them with care. And keep in mind: the reality might not all the time be flashy, but it surely all the time finds its technique to the sunshine.

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

References

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

[2] Tobacco producers’ defence in opposition to plaintiffs’ claims of most cancers causation: throwing mud on the wall and hoping a few of it would stick, A number of Authors, Nationwide Library of Drugs

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

Disclaimer

This put up 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 supposed message was precisely mirrored. All different makes use of of AI (as an illustration picture and pattern information technology) have been disclosed straight within the textual content.