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 sources are likely to current information storytelling in an overwhelmingly constructive 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 residence buildings in my neighborhood. Now, check out the picture 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’d possible concentrate on the fast environment, particularly as offered within the vendor’s images. Discover something uncommon? In all probability not, at the very least not straight away.

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 mean 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 bathroom proper there. Nonetheless be ok with the situation? Perhaps sure, possibly no. One factor is obvious: you’ll wish to know {that a} public rest room 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 might be introduced up in value negotiations.

This straightforward instance illustrates how simply tales (on this case, utilizing images) might be misinterpreted. From one angle, every part appears to be like superb, even inviting. Take a couple of steps to the best, and… whoops.

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

This submit 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 are able to keep away from that.

Knowledge storytelling is subjective

We frequently prefer to imagine 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 depart out,
• tips on 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,
• choosing applicable 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 knowledge. This opens the door to biases. If we aren’t cautious, we are able to simply cross the road from subjectivity into unethical storytelling.

This text examines the hidden biases embedded in information storytelling and the way we are able to 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. Instructed us by our grandparents, dad and mom, academics, mates, 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 strong 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 complicated points. Tales typically depend on emotion, selective element, and a transparent message, which may 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 purpose.

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

However right here’s the issue: until you have already got sturdy 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 practically 20 pages later. This can be a basic instance of how the construction of educational reporting can mute the story’s affect. It outcomes from the truth that information is offered 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 information factors (cherries 😊) to incorporate (and which to disregard) is among the strongest — and infrequently most missed — acts of bias. And maybe no business 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 accountability utilizing a sequence of arguments that, whereas generally legally strategic, had 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 varieties to problem common epidemiological proof.

It wasn’t our model.

• “Certain, tobacco could 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 exterior the slender 4-year window after they had been used, although 585 million packs had been bought throughout that point.

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

In a number of instances, comparable to Ierardi v. Lorillard and Lacy v. Lorillard, the protection admitted that cigarettes may 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 danger issue — no matter plausibility — to deflect from tobacco’s position.

There have been different danger components.

• In lots of lawsuits, firms pointed to different causes of sickness: asbestos, diesel fumes, alcohol, genetics, weight loss plan, 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 will probably conceal in authorized defenses, advertising copy, dashboards, and even tutorial experiences. However when solely the info that serves the story will get instructed, it stops being perception and begins turning into manipulation.

Bias 3: The Mirror within the Forest: How the Similar Knowledge Tells Completely 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 features”? Each might be correct. The distinction lies in emotional framing.

In essence, framing is a strategic storytelling method that may considerably affect 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 objectives. I current some examples in Desk 1.

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

Visuals simplify information, 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 mistaken chart kind: Selecting charts that confuse fairly than make clear — like 3D pie charts or inappropriate comparisons — makes it tougher to see the story the info tells.
2. Including distracting parts: Stuffing visuals with logos, decorations, darkish gridlines, or muddle hides the vital insights behind noise and visible overload.
3. Overusing colours: Utilizing too many colours can distract from the main target. With no clear colour hierarchy, nothing stands out, and the viewer is overwhelmed.
4. Random information ordering: Scrambling classes or time sequence 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 components of the info that assist your level, ignoring the complete story or contradicting proof.
8. Omitting visible cues: Eradicating labels, legends, gridlines, or axis scales to make information onerous to interpret, or onerous to problem.
9. Overloading charts: Packing an excessive amount of information into one chart might be distracting and complicated, particularly when vital information is buried in visible chaos.
10. Exhibiting 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 dimension 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, complicated, or overly creative charts which can be onerous to interpret and simple 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 educated 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 vital query: Are we utilizing AI to democratize perception, or to mass-produce narrative spin?

A latest BBC investigation discovered that main AI chatbots steadily 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 prison 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 information 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, comparable to “Vaping is all the time safer,” with out adequate context.

Anchoring bias causes individuals to fixate on the primary quantity offered—like a 20% forecast—distorting how subsequent info is interpreted. Omission bias arises when vital information is overlooked, for example, 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 might 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 3 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 had been the precise trigger.

The right way to “Unbias” Knowledge Storytelling

Each information story is a selection. 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 information 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 information isn’t just about readability or engagement—it’s about accountability. Each selection 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 details.

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

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

On this planet of knowledge 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 fairly 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 colleges are failing,” sparking concern or blame. However a extra cautious, analytical response could be, “What components might clarify this transformation? Might or not 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 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 area for interpretation, dialogue, and deeper pondering. This methodology guards towards false certainty and encourages a extra collaborative, considerate exploration of knowledge. A robust 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 accountability to acknowledge them. Presenting a counter-narrative—“right here’s one other method to have a look at this”—invitations vital pondering and builds credibility.

For instance, a chart could present that coronary heart illness charges are declining total. That looks like a hit. However a more in-depth look could 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—gives a extra complete and sincere image of the difficulty.

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 have to be used responsibly. Deceptive charts can distort notion by means of delicate tips, comparable to truncated axes that exaggerate variations, unlabeled items that obscure the size, or ornamental muddle 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 varieties that greatest match the info, 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 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 onerous 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 aren’t 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 best 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: Communicate 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. If 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 exhibits that you simply respect your viewers’s capability to have interaction with complexity, fairly than oversimplifying to keep up a clear narrative.

Uncertainty can come up from numerous sources, together with restricted pattern sizes, noisy or incomplete information, altering circumstances, or the assumptions inherent in predictive fashions. As an alternative of ignoring or smoothing over these limitations, good storytellers convey them to the forefront—visually and verbally. Doing so encourages vital pondering 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 info can’t inform us, we give extra weight to what it could actually. Under, I current a number of examples of how you can embrace info on uncertainty in your information story.

1. Replace on confidence intervals
   As an alternative of: “Income will develop by 15% subsequent quarter.”
   Use: “We undertaking 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 information indicators
   Use visible cues, comparable to light bars, dashed strains, or shaded areas, on charts to point gaps.
   Add footnotes: “Knowledge for Q2 is incomplete as a consequence of reporting delays.”
4. Mannequin assumptions
   Instance: “This forecast assumes no important change in person conduct or market circumstances.”
5. A number of situations
   Current best-case, worst-case, and most-likely situations to mirror a spread of doable outcomes.
6. Probabilistic language
   As an alternative of: “This can 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 information:
   “Outcomes are based mostly 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 components.”
10. Qualitative clarification
    Use captions or callouts in shows or dashboards:
    “Knowledge developments are indicative, however additional validation is required.”

You would possibly marvel, “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 info 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. Persons are way more prone 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 knowledge storytelling, these roots are your sources. A ravishing 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 is just not 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 warp the reality. Whether or not you’re a knowledge scientist, communicator, or decision-maker, your tales carry weight—not only for what they present, however for the way they’re instructed.

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 purpose 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’ll be able to inform is one which’s each clear and sincere.

In the long run, storytelling is just not about controlling the message—it’s about incomes belief. And belief, as soon as misplaced, is just not simply gained again. So select your tales fastidiously. Form them with care. And bear in mind: the reality could not all the time be flashy, nevertheless it all the time finds its method to the sunshine.

And yet another factor: when 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 towards plaintiffs’ claims of most cancers causation: throwing mud on the wall and hoping a few of it’s going to stick, A number of Authors, Nationwide Library of Medication

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

Disclaimer

This submit was initially written utilizing Microsoft Phrase, and the spelling and grammar had 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 (for example picture and pattern information technology) had been disclosed instantly within the textual content.