The American legal system is based around the concept of presumption of innocence. (At least, it’s supposed to be…) “Innocent until proven guilty.” A verdict is not rendered until after a fair trial has taken place, at which point the judge / jury will render a verdict of guilty or not guilty.
Notice the particularity of that phrasing, “guilty” or “not guilty.” You never hear “guilty” or “innocent.” This is intentional because the phrasing “not guilty” is essentially saying “the court does not have enough evidence to deem you guilty.” Perhaps the best example that makes the most sense here is of OJ Simpson. OJ was pronounced “not guilty” of murdering his wife because the court couldn’t find enough conclusive evidence to pronounce him guilty beyond a reasonable doubt. (“Reasonable doubt” is also a specific term commonly used in the U.S. criminal legal system.)
(I know way too much about the legal system for my given profession…)
But that didn’t necessarily mean the court thought OJ was innocent. Reasonable doubt is the highest burden of proof in trials like this, so if OJ were put in a trial where the burden of proof was lessened, it’s likely he would have been deemed guilty.
Let’s shift gears and talk about null hypotheses. Null hypothesis is a term used in the statistical community to refer to a default position you’re taking on a given matter. Don’t worry if the term “null” throws you off because it did to me, too, when I first learned about it. A null hypothesis, for example, can be “We believe exactly 80% of people who love ice cream hate pizza.” Based on this null hypothesis, you can create alternative hypotheses that state something like “Less than 80% of people who love ice cream hate pizza” or “Greater than 80% of people who love ice cream hate pizza.”
And with our hypotheses in tow, we’re off to the races! Specifically, you (the researcher) will start examining many samples across a population (or a whole population itself) to analyze whether or not the data supports your null hypothesis.
Unless you’re able to go through an entire population (which most often is unfeasible), you’ll be reliant on samples you think best represent your population. Obviously, your samples will make some big assumptions, so as a way to sort of cushion your findings, you’ll set a sort of threshold of error around your findings, known as a margin of error. That margin of error is often set at 5% or less, so as long as your sample statistics don’t exceed your margin of error threshold, then they should still provide good evidence to support/not support your null hypothesis.
But just like with the legal system’s guilty vs. not guilty, you either reject a null hypothesis or fail to reject the null hypothesis. And rejecting the null hypothesis doesn’t automatically make something else true. This mirrors the “not guilty” verdict quite well. Rejecting a null hypothesis is basically saying “we don’t have enough evidence to support the tenets of this hypothesis.” What it doesn’t do is automagically say there is enough evidence to support one of the alternative hypotheses.
Shifting gears once again, let’s now talk about the origin of life, the universe, and everything in it.
Popular opinion amongst many people is that everything was created as a sort of random happenstance. There is no god, and the creation story told in Genesis is wrong.
Now, let’s examine that through the statistical lens we just learned. In this case, the null hypothesis would be, “There is a 100% chance the world was created by random chance.” But if you look at the evidence to support this hypothesis, it doesn’t line up. One site I found quoted that the probability of the Earth being the way it is is 1-in-700 quintillion. I had to look up what a quintillion is, and it is 1 followed by 18 zeroes. Even setting an extremely high margin of error, the evidence to support this null hypothesis doesn’t come close to supporting it.
Statistically speaking, we have no choice but to reject this null hypothesis.
Two words of note here. First, just because we don’t have enough evidence to support this null hypothesis today doesn’t mean it’s incorrect. In the statistical community, there are such things as false positives and false negatives, which basically state, “Our evidence could have been wrong, and the opposite of what we stated might actually be true.” That said, rejecting this null hypothesis could be a false negative, and it’s not necessarily impossible that we might find new evidence that reverses this in the future.
What I find more important is what rejecting this null hypothesis DOESN’T mean. Remember, rejecting a null hypothesis doesn’t automagically make an alternative hypothesis true. In other words, rejecting the null hypothesis of the world having been created by chance doesn’t automatically make the alt hypothesis of “God created the world” true.
Actually, let’s put that through the ringer. What if we make “God created the world” our null hypothesis? Well, here’s what the evidence has to say about that: ¯\_(ツ)_/¯. We have no way of testing that, so technically, we can’t fail to reject nor reject this null hypothesis because the evidence to support this cannot be empirically or statistically verified. (At least in the case of the former null hypothesis, we could somewhat examine that likelihood from a purely scientific perspective.)
We have to remember that other alternative hypotheses exist. Many people, including the great Elon Musk, totally buy into the idea of a simulation theory. Other people acknowledge the existence of an “intelligent designer” but are leery to give that designer a name.
So, what do we make of all this?
Regardless of what you believe, we have to admit we truly don’t know the origin of the universe. And if we can’t definitively pinpoint one thing over another, then you should diligently entertain all options.
And that’s what I hope you walk away with from this post. We talked about the origin of the universe here, but really, it expands to all aspects of life: we’re notoriously bad about closing ourselves off to other options in light of what we think is 100% true. I love the phrase from the video game series Assassin’s Creed that states “Nothing is true, everything is permitted” because it opens that door to new possibilities, new ideas, and new options in light of what you may already hold as being true.
If you want to know one of the keys to my personal success, it’s this. It’s the humility to say “Because I can’t be 100% sure of anything, I must explore everything.” And it’s in that exploration of everything that I learn new ideas that have helped to continuously develop my path over time.
Phew, that was a long and heavy post! If you made it to the end, pat yourself on the back and treat yourself to a large sugar-free iced vanilla coffee from McDonalds, because empirically speaking, McDonalds has the best iced coffee.
And I’d be willing to bet that’s a null hypothesis we’d fail to reject. *wink*