A narrative created after a specific result often downplays alternative scenarios that could have happened, making success and failure seem more predictable than they are.

Outcomes that seem obvious in hindsight are often unknown at the time of the decision, such as the modern PC, Google, or Harry Potter, that expert investors initially rejected. Often, not even the owners can accurately predict their own potential.

Stories can suggest a causal link when the link doesn't exist. For example, urban legends like the Sports Illustrated magazine state that individuals or teams who appear on the cover will subsequently experience bad luck.

Similarly, managers can form faulty beliefs about the effects of praise and punishment, especially when the performers they praise go on to perform worse and those they punish afterwards improve.

Some stories fail to see the existing relationship when causes and effects are separated by time. Stories can misinterpret investments that feature worse-before-better dynamics.

New leaders may receive acclaim or blame for results that happen right after their appointment even though it may be due to the previous administrations.

Stories based on the past can become outdated when situations change suddenly and drastically. Traditions built on them often persists long after.

• A few decades ago, a college degree would almost certainly guarantee a lucrative career. This is no longer true, but the idea is causing a growing student debt crisis.
• Companies that seem invincible can quickly come to a fall, especially when processes grow non-linearly while competent decision-makers fail to notice, as with Myspace and Nokia.

Data-based analyses are often turned into a story for easier understanding and adoption. But the stories are limiting the discussion to the average statistical effects.

Stories based on the average are often only valid for the average of the samples and might hide significant risks and nuances around an expected outcome.

Personal experience and notable events may be appealing but often tend to be unrepresentative.

In reality, the more unique the observation, the less likely it is to generalise. When dealing with complex decisions, many organisations now favour data-based algorithms over experience-based narratives.

While success stories are motivating, they could lead to a false belief that success is more controllable and predictable than it is.

• The common traits of successful people and organisations are ubiquitous. These same traits may be equally present in the less successful.
• Stories that emphasise how talented and hardworking the successful are ignores scores of failures with similar skills and work ethics. The success could be due to circumstantial and random reasons.

Stories often focus on the outcomes that can be seen while ignoring the underlying processes.

• This leads to widespread blindness to possible deceptive behaviours that contributed to the outcomes. Examples include Ponzi schemes and fraudulent business practices.
• It leads to a misunderstanding of how innovation works. Focusing on the individuals' creativity glorifies the final version while ignoring the underlying collaborative processes by risk-taking entrepreneurs.

Stories can be misleading despite providing information.

• Some stories can feature a combination of warning signs. The more warning signs a story shows, the more scepticism is needed.
• Time-related biases and selection problems can happen even when every other part of the story is correct.

The solution isn't to stop telling stories as they provide vital benefits. But astute decision-makers can take convincing narratives as theories to be scrutinized, rather than absolute truths.