The 4-Hour Body_ An Uncommon Guide to Ra - Timothy Ferriss [203]
I use myself as a single subject, and (with a few exceptions) I neither randomize nor create a control. Some scientists will, no doubt, have a field day picking these self-experiments apart.
This doesn’t bother me, and it shouldn’t bother you.
The goal of this chapter is simple: since we often use published research as a starting point for self-experimentation, we want to ensure that we don’t take false leads from tricksters or misinformed journalists with good intentions. Understanding the Big Five questions and the hallmarks of sensationalism puts you in a rare group: those who can depend on themselves, not the media, for nutritional guidance.
This opens doors that we can then crowbar and leverage for incredible effect.
My goal for the book is not, first and foremost, to identify the single variables that produce target changes. That is often the goal of clinical research for publication, but experimentation for self-improvement is a different beast.
It may be that alpha-lipoic acid does nothing in a given fat-loss cocktail; perhaps it’s the angle of an exercise and not the load that triggers muscular gain; or it could be that spinach has none of the effects I predict, but other foods in the prescribed meal do. The exact mechanism doesn’t much matter if we get the effects we want … without side effects.
Dr. Martin Luther King Jr. famously wrote that “justice too long delayed is justice denied.” In the world of self-experimentation, where the outcomes are of personal importance, results too long delayed are results denied. This doesn’t mean being haphazard. It’s more than possible to tinker without hurting yourself. It means, however, that waiting for perfect conditions often means waiting forever.
In the world I live in, people want to lose fat or improve sexual performance now, not in five or ten years.
Let the journals catch up later—you don’t have to wait.
P-Value: One Number to Understand
Statistical thinking will one day be as necessary for effective citizenship as the ability to read and write.
—H. G. Wells, who created national hysteria with his radio adaptation of his science fiction book The War of the Worlds
British MD and quack buster Ben Goldacre, contributor of the next chapter, is well known for illustrating how people can be fooled by randomness. He uses the following example:
If you go to a cocktail party, what’s the likelihood that two people in a group of 23 will share the same birthday? One in 100? One in 50? In fact, it’s one in two. Fifty percent.
To become better at spotting randomness for what it is, it’s important to understand the concept of “p-value,” which you’ll see in all good research studies. It answers the question: how confident are we that this result wasn’t due to random chance?
To demonstrate (or imply) cause-and-effect, the gold standard for studies is a p-value of less than 0.05 (p < 0.05), which means a less than 5% likelihood that the result can be attributed to chance. A p-value of less than 0.05 is also what most scientists mean when they say something is “statistically significant.”
An example makes this easy to understand.
Let’s say you are a professional coin flipper, but you’re unethical. In hopes of dominating the coin-flipping gambling circuit, you’ve engineered a quarter that should come up heads more often than a normal quarter. To test it, you flip it and a normal quarter 100 times, and the results seem clear: the “normal” quarter came up heads 50 times, and your designer quarter came up heads 60 times!
Should you take out a second mortgage and head to Vegas?
The above sample size estimation tool, created by the web design and analytics firm WebShare, says: probably not, if you want to keep the house.
If we look at 20% improvement (60 flips vs. 50 flips = 10 more flips) at the top and scan down to see how many coin flips you’d need per coin to be 95% confident in your results (p = 0.05), you’d need