Null Hypothesis – Statistics Basics

00:01 going to talk about biases in more depth in a further lecture.

00:01 So now let’s switch gears a bit and talk about the null hypothesis, which is one of the foundational ideas for doing statistics, what is the null hypothesis? It is a statement, it's a statement that there is no relationship between the variables that we’re trying to test for. It is important that you understand that the null hypothesis always says, there is no relationship. The null allows us to assess our statistical tests and tells us whether or not to reject or fail to reject our null hypothesis. I'm a bit of a stickler for protocol and philosophy, so I say fail to reject, some people say accept. For reasons that I won't get into here, philosophically we never actually accept a null hypothesis, we either reject it or we fail to reject it. Remember, the null hypothesis says, nothing is going on, there is no relationship between the things we are measuring. What I want to do is to be able to reject that hypothesis. If I reject that hypothesis, I know something is going on, I know I've found something interesting in my test. I can write the null hypothesis mathematically like this, H0 is notation for the null hypothesis, the Greek letter µ is notation a mean, an average in one group. So here I’m saying that the mean measurements in two separate groups are the same. Maybe I’m running a randomized controlled trial, maybe I’m looking at the mean responses in my treatment group versus my placebo group. My null hypothesis says, they're exactly the same. In other words, I found no effect, nothing is going on, I'm assuming that the thing I'm measuring has no effect whatsoever. That's my null hypothesis.

01:44 I'm seeking to reject that null hypothesis.

01:50 In this example, I’ve shown the comparison group to be a placebo group. That’s a specific example. More generally, calling it a control group would be more accurate.