You’re developing a business strategy. What common logical fallacies should you avoid?

Confirmation bias is like wearing blinders to only see what you want to see. In business strategy, it's deadly. I learned that ignoring data that challenges your initial ideas is a one-way ticket to failure. My advice? Embrace the uncomfortable truths early on—it might sting, but it's a surefire way to build a strategy that stands strong.

In crafting a business strategy, dodge common pitfalls: - Don't just echo what's popular (Bandwagon) or cling to the past (Tradition). - Ignore noise, focus on facts. - Avoid assuming cause from sequence (Post Hoc) or simplifying arguments (Straw Man). Decisions should be evidence-based, not swayed by authority (Appeal to Authority) or personal attacks (Ad Hominem). Embrace diverse perspectives, question assumptions, and build on solid, logical foundations

Confirmation Bias Focusing only on information that confirms preconceived beliefs or desires while ignoring contradictory evidence. Seek out diverse perspectives and consider all available data objectively.

Confirmation bias is natural but particularly bad in business strategy. You will tend to favour information that confirms your preconceived notions, disregarding evidence to the contrary. You need to be conscious of this because such an approach can significantly skew strategic decision-making. It will lead to strategies that are not grounded in reality. To fight this, actively seek out diverse perspectives and challenge your assumptions, ensuring a more balanced and effective strategy.

When developing a business strategy, avoid common logical fallacies such as the false cause fallacy (assuming causation without evidence), the appeal to authority fallacy (relying solely on authority figures), the anecdotal fallacy (using isolated anecdotes as evidence), the slippery slope fallacy (predicting extreme consequences without justification), and the bandwagon fallacy (believing something is true because many people do.

"The greatest teacher, failure is." - Yoda, Star Wars. The survival bias is a cognitive trap that focuses on successful outcomes while overlooking failures. This bias can lead to overestimating success probabilities, underestimating challenges, and ignoring lessons from mistakes. To counteract survival bias, examine both successes and failures, consider base rates and probabilities, and learn from all outcomes. How do you ensure to learn from every experience and avoid survival bias? Share your strategies and engage with peers for collective learning!

Survivorship bias is the 'highlight reel' of business strategies. Imagine watching only the game-winning shots in basketball to learn the sport. In strategy, focus on the misses too. Learn from the businesses that stumbled, their missteps reveal the real hurdles you might face.

Survivorship bias occurs when one focuses only on successful outcomes while ignoring failures, leading to an incomplete or skewed understanding of the situation. To avoid this bias, it's important to analyze both successful and unsuccessful cases to gain a comprehensive understanding of factors contributing to outcomes.

In healthcare, especially in the heavily regulated spaces I've worked in, projects can quickly become entangled with complex webs of approvals, sunk costs, and a reluctance to change course. The sunk cost fallacy can lead to stubbornly persisting with a suboptimal strategy simply because of the time, money, or effort already invested. It's important to remember that the goal of any healthcare initiative is ultimately to improve patient outcomes. It's a difficult but necessary mental shift, and something I learned from observing how certain medications are sometimes discontinued despite extensive investment if patient outcomes weren't where they needed to be.

The sunk cost fallacy involves continuing an endeavor or investment solely because of the resources already invested, regardless of the likelihood of success. To avoid this fallacy, decisions should be based on future potential rather than past investments. It's essential to evaluate options based on their expected returns and costs going forward, rather than considering past investments that cannot be recovered.

The healthcare industry is inherently complex, riddled with intertwined challenges and opportunities. It's easy to fall into the trap of oversimplification and framing choices as either/or scenarios. One example is the perceived dichotomy between focusing on short-term profitability versus long-term patient outcomes. In my experience, the most effective strategies tend to consider the bigger picture. Instead of getting stuck in a false dilemma mindset, I try to reframe choices by asking, "how can we achieve both?" This mindset shifts the focus toward finding innovative solutions that may not be immediately obvious, leading to strategies that are not only more successful but also more sustainable from a healthcare perspective.

Also known as the black-and-white fallacy, this occurs when only two options are presented as possibilities when, in fact, there are additional alternatives. To avoid this fallacy, it's important to explore a wide range of options and consider potential alternatives beyond the initial choices presented.

I see it times and times again: When making a business plan, avoid jumping to conclusions too quickly based on too little information. This can make your strategy weak because it is based on wrong assumptions instead of what is really happening. To prevent this, always use lots of different information and carefully study the market. Talking to many people for their views and looking closely at what competitors are doing will also help make your plan stronger and more realistic.

Hasty Generalization Drawing broad conclusions based on insufficient evidence. Make sure your strategic decisions are based on comprehensive data and analysis.

Many big businesses today were started by founders trying to solve their own problems. This can create a bias among budding entrepreneurs that it is a repeatable model. It can be, but with certain conditions. Hasty generalization can lead to disproportionate amount of time being spent in irrational ideas. To resolve this, these ideas must be tested with the largest datasets possible having diverse variables within the targeted demographic.

This fallacy involves drawing broad conclusions based on insufficient or biased evidence. To avoid hasty generalizations, decisions should be grounded in robust data and thorough analysis rather than hastily drawing conclusions from limited information.

Bandwagon Fallacy This is the tendency to adopt a strategy or belief simply because it is popular or trending, rather than because it has been critically evaluated for its applicability to your specific context. Anchoring Bias This occurs when decision-makers rely too heavily on the first piece of information they receive (the "anchor") when making decisions, even if it's irrelevant or not the most informative. The Dunning-Kruger Effect This cognitive bias is where individuals with limited knowledge or competence in a domain overestimate their own abilities. In strategy development, this can lead to overly optimistic assessments of a business's capabilities or market opportunities.

As a pharmacist, the importance of differentiating between correlation and causation was ingrained in me from the start. This concept holds true in developing robust healthcare business strategies. While data-driven insights are vital, it's equally important not to assume that a simple correlation implies a direct cause-and-effect relationship. Rushing to implement strategies based on presumed causality without thoroughly considering potential confounding variables can have unintended consequences. I've found it's always crucial to invest time and resources into a more nuanced analysis of the data, potentially even employing controlled studies, whenever possible, to separate true causation from mere correlation.

This fallacy involves mistakenly assuming that because two variables are correlated, one must cause the other. To avoid this fallacy, it's important to conduct rigorous analysis to establish causation through controlled experiments or carefully designed observational studies, rather than relying solely on observed correlations.