Is it Ockham or Occam?
When two explanations fit the facts equally well, which one should you trust? The one with fewer moving parts. In philosophy, a razor is a principle that allows one to eliminate or shave away unlikely explanations to a logical problem, thus avoiding unnecessary considerations.
The principle is credited to the early 14th-century English Franciscan friar William of Ockham, a philosopher and theologian. It is also known as the Law of Economy or the Law of Parsimony.
Ockham’s razor, also spelled Occam’s razor, is a problem-solving principle stating that when presented with competing theories as possible explanations for the same question, and assuming all other considerations are equal, the simplest of those competing explanations is generally the better one and should therefore be preferred over the more complex alternatives.
While Ockham’s razor more closely reflects the name of William of Ockham, from whom the principle takes its name, Occam’s razor is the more commonly used spelling today.
In Ockham’s own words: “Plurality should not be posited without necessity,” “Entities are not to be multiplied without necessity,” and “It is futile to do with more things that which can be done with fewer.”
When faced with multiple possible explanations, the principle holds that the simplest one is generally the most likely to be correct. The principle does not suggest that the simplest explanation is always correct. Rather, it suggests starting with the explanation that requires the fewest assumptions and introducing additional complexity only when the evidence requires it.
However, the principle is not a commandment and is not meant to take precedence over sound logic.
The concept was invoked by philosophers and scientists before Ockham, including Aristotle and Maimonides, as well as many who followed him, such as Isaac Newton. However, Ockham referred to it and applied it so frequently that the principle was eventually named after him, centuries after his death.
From Philosophy to Science and Business
Beyond philosophy and religion, Occam’s razor has been applied in science as a heuristic for model development, as well as in other areas of life and business that involve problem-solving and the selection of a single solution from multiple, equally plausible hypotheses.
In physics, Albert Einstein employed the principle of parsimony in formulating his special theory of relativity, famously advising: “Make everything as simple as possible, but not simpler.”
Or, as Isaac Newton wrote, “We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearance.”
In mathematics, where each assumption introduces a possibility of error, any assumption that does not improve the explanation is removed, since keeping it would only increase the risk of mistakes.
The principle is also applied in evolutionary biology, psychology, religion, penal theory, probability theory, and statistics.
The principle is also applied in medicine. The well-known clinical reminder, “When you hear hoofbeats, think horses, not zebras,” captures it well. Common causes should be considered before rare ones when forming a diagnosis.
In business, the principle shows up constantly. A company’s sales are declining. Leadership considers repositioning the brand, restructuring the team, and overhauling the product. But a closer look reveals that a key competitor dropped their price three months ago. Before redesigning the organization, the simpler explanation deserves consideration. So does the simpler response.
The same logic applies to internal operations. When a process is broken, the instinct is often to build a new system, hire a specialist, or commission a review. But frequently the issue is simpler: unclear ownership, a missing step in communication, or a policy that no longer reflects how the team actually works. Addressing the simpler explanation first saves time, money, and organizational energy.
In leadership, the principle is equally useful. When a team is underperforming, it is tempting to question strategy, structure, and culture simultaneously. But the simpler question is often the right starting point: does the team have what it needs to do the work? Clear goals, adequate resources, and honest feedback resolve more performance problems than restructuring does.
Occam’s razor does not eliminate the need for thorough analysis. It provides a starting point. Begin with the explanation that requires the fewest assumptions. Test it. If it holds, act on it. If it does not, add the next layer of complexity. This approach reduces the risk of solving the wrong problem and keeps decision-making grounded in what the evidence actually supports rather than what feels comprehensive.
Over the years, some mathematicians have criticized the principle for oversimplification and warned against reducing complexity to the point of inadequacy, arguing that attempting to solve a problem with less than what was required was futile.
While the mathematical debate belongs to mathematicians, there is practical value for Occam’s razor in business and in life.
Complexity Is Not Rigor
Organizations often equate complexity with thoroughness. A longer analysis feels more credible. A multi-step solution signals effort. A detailed framework suggests expertise. But complexity introduced without necessity is not rigor. It is noise, and it carries real costs: slower decisions, harder alignment, and a higher chance of solving the wrong problem entirely.
Occam’s razor is a useful corrective. It does not discourage deep thinking. It discourages unnecessary thinking. The distinction matters. A leader who considers every possible explanation before acting is not being thorough. A leader who starts with the simplest plausible explanation, tests it, and adds complexity only when the evidence demands it is being both thorough and efficient.
The mathematicians who criticized the principle for oversimplification were not wrong to raise the concern. Reducing a problem beyond what it requires is as dangerous as overcomplicating it. The goal is not the fewest possible assumptions. It is the fewest unnecessary ones.
In practice, that means asking a simple question before reaching for a complex solution: what is the most straightforward explanation that fits the available evidence? Start there. Test it. If the evidence supports it, act. If it does not, add complexity only where necessary. More often than not, the right answer is closer than it first appears.