Of course we want to promote healthy skeptical attitudes ("healthy" being a key word here, but that's another post) in the general public. Skepticism is a critical part of the scientific mindset, and a properly skeptical outlook allows one to - essentially - not waste time and energy believing statements that aren't likely to be true.

But the application of that skeptical mindset can lead to more problems than it solves, especially when it comes to science communication. When does a debate become an argument? When it gets personal; when it shifts from discussing the relative merits or demerits of a particular position to the rightness or wrongness of what one believes. Once that shift happens, we haven't hit the iceberg yet...but it's hard to turn that ship around. If we're actually trying to educate, persuade, or inform, we're basically done.

Here's the problem: skepticism isn't a measure of the truthfulness of a statement, but its believability. It's perfectly acceptable to say "I don't believe that statement, but that doesn't make you wrong." Go ahead, try it out, it won't hurt. Statements can be 100% absolutely true but not able to be believed because of lack of evidence, and we all have different thresholds.

This shifts the discussion from things that a person might hold near and dear to their heart - and thus will righteously defend - back to the realm of the abstract. In this case, the nature of evidentiary standards.

In short, a skeptical person ought to be known having a high bar for believing statements, but it's nothing personal, and they won't hold it against you.

I don't know how, when, or why this myth emerged that Einstein was a poor or lazy student, or that he valued the creative side of humanity over the analytical. Perhaps it's because Einstein himself often downplayed his own mathematical prowess and emphasized the power of imagination. Perhaps it's because Einstein's genius is so unattainable to us mere mortals that we have to comfort ourselves otherwise we feel eternally insignificant.

It is true that Einstein was remarkably creative. One could safely argue that some of his contemporaries were at least as smart as the man himself, but nobody else thought the same way he did. He made brilliant and unparalleled leaps of insight, discovering hidden relationships with his favorite trick: the thought experiment. By imagining some scenario (racing a beam of light, falling off a tall tower, and so on) he was able to make astounding advances in our understanding of nature.

But while thought experiments begin with creative thinking, they don't end there. Missing from the typical narrative is the exceedingly competent analytical mind that Einstein brought to the problems. He followed his thought experiments to their logical ends, and worked to express those creative insights in the language of mathematics.

It took Einstein seven years of dogged pursuit to go from simple thought experiments to General Relativity, our modern theory of gravity. Those years were full of blind alleys, wrong turns, stubborn biases that held him back, misgivings and unease about the results, and mathematics so advanced that few people could even keep up. And through it all persistence, persistence, persistence.

So I think the lesson here is that the key to success isn't just creativity. That is essential, for sure, but not the only ingredient. You also need to add to the mix some critical thinking, logic, analytics, and perseverance. Then you'll find the potent concoction that made Einstein so remarkable.

You've probably encountered the phrase "extraordinary claims require extraordinary evidence." Well, it's not just some pithy saying that relieves you of the burden of having to believe every random statement you might encounter in your life. It's actually a handy summary of a very powerful approach to statistics.

The heart here is something called Bayes' theorem, named after the Reverend Thomas Bayes, who was kicking around cool ideas in the first half of the eighteenth century. It relates some statement you're trying to test ("giant stars die as supernovas", "jelly beans cause cancer", "aliens visit Earth because their star exploded and they're going to steal our jelly beans", etc.) to the individual probabilities of each component (the number of stars you observe dying, the number of people eating jelly beans, and so on).

Of course I'm radically simplifying this, but the key takeaway is that Bayesian statistics folds in your existing knowledge of the problem directly into the math, and provides a way to create outcomes that are an updated view of the world based on any new information or experiments. Thus if you don't have a lot of info already, a quick experiment can start to point you in the right direction. But if there is already a huge amount of knowledge about a relationship, it will take a tremendous amount of good evidence to change that perspective.

Unfortunately while simple to state the application of Bayesian statistics gets really complicated really fast, and it's only recently that we've had the computational power to crunch through the intense calculations involved. Which is good. The more we move away from simple-to-apply but simple-to-abuse methods (*cough* p-values *cough*), the better.