This post is the second in a series about geeky mysticism. The first post is here.
You’ve probably heard the story about the starfish-throwing little kid, who is busy throwing stranded starfish into the sea. An adult comes along and says, “There are so many starfish on the beach! Why bother? How can you possibly make a difference?” And the kid bends over to throw another starfish and says, “It made a difference to that one.”
That’s how I think about all the tools in my toolkit. It only has to make a difference to one person to be worth learning, investigating, and keeping around. This is how I figure that out.
Traditional scientific method has a series of steps. You probably remember them from 5th grade science class: you write down what you think is true or what you’re going to test. Then you write down what you plan to do to test it. You make a list of your materials. You do the experiment and make notes about what actually happens and any new thoughts or changes you needed to make. And then you write your conclusion. In the real world, 98% of conclusions seem to be, “Well look, there’s some stuff we didn’t anticipate that merits further study.”
Then they redesign and run the experiment again.
And in the world of pharmaceuticals and medical study and other things that deal with people and not, say, chemicals or rocks, there’s this thing called a controlled double-blind study.
What that means is that you have a group of people who don’t get the treatment you’re testing, to see what would happen if you did nothing. And no one, not the researchers or the participants, knows which participants aren’t getting the treatment and which ones are. So, for example, if you’re testing a new medicine for depression, half the people get pills that will not chemically interact with the body and half the people get the medicine, but the researchers don’t know who is who until they start analyzing the data.
And then they look for what they call a statistically significant result. They’re looking to see that a more-than-random number of people (usually above 5%) had a result.
But my world is a little different.
Because all experience is data, and people vary.
And magic and energy work and prayer are all basically the same thing, the effects of which have been investigated (both well and badly) for thousands of years across all or nearly all cultures.
So what that means is that if it works for one person, I’m interested. I don’t need 500 people in my study, and I don’t care if it’s double blind, especially since the focus and energy of the person matters so much. Your belief matters when it comes to magic.
Here’s where my process deviates really really far from standard scientific process. Because most scientists don’t usually act like a result matters unless they saw a lot of the same result. Fifty percent of study participants, or seventy-five. Five percent barely meets the test for statistically significant. Below statistically significant it might not have happened at all. It might be a mathematical error. Now the issue of how a recorded data point can become a mathematical error (and therefore nonexistent) aside, in my world if something works for me, even if it’s just for me, even if I only tried it twice, it goes on the list of things that work for me, and the result is real.
It means that one time that reiki helped my cramps was enough for me.
It means that one month when I kept pulling the same tarot cards was enough for me.
I share my experience and I make no promises. But testing doesn’t have to be a giant formal study, because if it works for me, I will use it regardless of whether it works for other people, and I will tell people about that, and invite them to run their own experiments. I don’t care if I’m in a small population that’s a fraction of 1 percent. What I care about is whether I have seen results. And I encourage all the people around me to do the same.
Want to find out what kinds of stuff I do, and why I do it? Join Marcia and me on Embodiment for Brainiacs, Nov 1, from anywhere! Sign up here: embodiment-for-brainiacs