How to Find What Works Every Time

“[F]rom my knowledge of the world that I see around me, I think that it is much more likely that the reports of flying saucers are the result of the known irrational characteristics of terrestrial intelligence rather than the unknown rational efforts of extraterrestrial intelligence.”

—Richard Feynman, Feynman on Scientific Method

The great physicist and teacher Richard Feynman once explained the scientific method as follows: First, you make a guess (a hypothesis). Next, you calculate the consequences of that guess. And then, you run experiments to test whether nature agrees with those consequences.

If your experiments agree with your guess, you have a good theory. If they disagree, then the theory is wrong. Because there are infinite possibilities and experiments, a theory can never be more than a theory—no matter how many times your experiments agree with your guess. But it only takes one non-correlating observation to undermine it permanently.

Or, as Feynman says, “[W]e are never right. We can only be sure we’re wrong.”

In the same lecture, he goes on to poke at non-scientific methods of predicting outcomes. “You cannot prove a vague theory wrong,” he says. If your guess is vague and the methods you use to test it are vague and indefinite, then that theory can never be proved wrong. That’s bad science.

This distinction matters for anybody who needs to make decisions about how to spend finite resources to achieve a desired outcome.

Think about it: no one can predict the future, yet any time we allocate our time, energy, and money, we make some guess—a hypothesis—as to the value of the return on our investment. The farther into the future the hoped-for return, the more uncertainty surrounds our initial investments.

This problem is ubiquitous to all kinds of leaders and managers, but too many make the mistake of vagueness in their predictions because they are afraid of being wrong. But if we take Feynman’s view of good science, they really should be afraid of being unproveably right. When you are unproveably right, generally, you have no idea what is working or why or, importantly, what will work when things change.

The cure for vagueness is quantification, which is what I call the minimum effective dose for maximum impact.

Minimum Effective Dose or the Scientific Method for Allocating Resources

Every big goal is a composite of smaller goals and actions that, when completed, will equal the big goal. That is a simple idea, but if the big goal is three to five years away, how should we decide what actions to take today?

You basically have two choices. The first is trial and error, where you take a shot in the dark and see what happens. If it works, you keep doing that; if not, you try something else. Trial and error can work for a while, especially if your shot in the dark is a shotgun blast, where you throw all your resources at the goal and hope for progress.

The problem with trial and error is that if it works, you don’t really know why; if it doesn’t work, you don’t have good data on which to base a new strategy.

The other option is to find the minimum effective dose (or MED), which replaces trial and error with a meticulous process of observation and analysis. The difference is that where you start with MED and how you approach every allocation of resources is driven by the need to create data and test whether that allocation of resources was beneficial.

The name “minimum effective dose” comes from the way doctors may titrate new medications. The doctor starts with a low dose, follows up to see the patient’s response, and adjusts the dosage accordingly. Since every medication has a minimum threshold for its benefits and a maximum tolerance, this method seeks to find the most effective dosage with the least potential for harm.

Applied to resource allocation, MED seeks to trump overly complex and complicated approaches by controlling as many variables as possible and working to find the sweet spot for the maximum impact on a person’s or company’s daily activities.

MED Principles for Successful Titration of Resources

It does not matter whether your goal is highly personal or involves teams of people. MED requires you to think like a CEO when allocating time, energy, money, and other resources. I have found that there are a few principles that help the process, no matter what field you are dealing with:

1. Simplicity over complexity. Start with the simplest allocation of resources that can affect change. While many tasks require complex or multi-faceted approaches, you can always look for the simplest steps first and prioritize those. The goal is to move from simple to complex over time.
2. Economy over excess. Economy of time, energy, money, and other resources will allow you to improve your return on investment and provide you with feedback on how to spend resources in the future.
3. Effort over easy. Simple and economical solutions do not work without effort. You must not try to trade resources for effort, spending time or money to reduce the effort you or your team puts into the tasks.

Simplicity + Economy + Effort = Peak Return on Investment

When we employ these principles, the goal is to find the sweet spot where you’ve maximized your return on investment for the least amount of resources. Then, stay there for as long as possible or until you’ve reached your goal.

Using MED, we can avoid making blind guesses and minimize mistakes and their associated costs, but that only works if we are not afraid to make mistakes. Making mistakes is one of the few ways to learn the right ways of doing things. Fear of mistakes leads to short-lived or one-off successes unless you are very lucky.

So, as with the scientific method, the key to allocating resources effectively in the long run is not about avoiding mistakes but about making the right kinds of mistakes. Start low, control your variables, and work your way toward the complexity that fits your problems.

By applying the minimum effective dose, you ensure that every action yields useful data and every mistake refines your approach.