Steve Tippins explains the difference between descriptive statistics, which summarize data like percentages and averages, and inferential statistics, which use samples to predict larger populations. Larger sample sizes make predictions reliable; small samples can mislead.
Let's talk about statistics, and I know that's scary, but let's talk about descriptive versus inferential statistics. I'm not going to go deep into this. We're not going to talk about formulas and all these other potential parts of this. We're not talking about significance. I just want to give you a simple overview of descriptive versus inferential statistics. Descriptive. 42% of the people voted for John Doe. 57% of the people voted for Jane Smith. 1% of the people voted for another candidate. It's just averages, means, modes, things like that. It doesn't tell me a lot. It doesn't mean I can translate that to other situations. But, you know, the average temperature on June 21st Is 78 degrees here, or the, the lowest temperature ever was -38, or the highest temperature. These are just things that I get to come up with a picture. Okay? They describe the situation. We can do those on just about anything. The more numbers we have, the more likely we are to understand it and get a good picture. It's like flipping a coin. It's heads or tails. If I flip a coin 3 times in a row and it's been heads, what's the chance it's going to be heads next time? Well, it's 50% because each one's individual. The 3 heads before don't influence this. But if I flipped a coin 300,000 times and it's a fair coin, it's going to be a pretty good bet that I'm going to be somewhere around 150,000 heads and somewhere around 150,000 tails. But if I've got a low, small number, it's not unheard of to have it be less than, you know, not 50/50. Inferential statistics are when we calculate things. You might want to think of confidence intervals. And inferential, I'm making predictions. Oh, well, you know, this, I'm 95% confident That this answer is an answer that is supported and is an answer that is, you know, people would provide. Because I've looked at lots of different things. It's not the average answer. It's an answer that falls within a range, or it's a number that falls within a range. So rainfall, of saying, oh, the average rainfall is 12 inches in this month, or the rainfall, I'm 95% confident That the rainfall this month will be between 8 inches and 16 inches. So now I get to make inferences. I get to use this beyond the scope of just describing something. We do statistical analysis, regression, ANOVA, all these types of things to be able to take information from a sample and project it out. To a population. That's the huge difference between descriptive statistics— just describes it. Inferential, I get to make decisions from. I infer from it. If you just remember that, I infer from this. This one just tells me what it is, what the temperature is. If I can make inferences, it's an inferential statistic. I know that wasn't deep or profound, but a lot of people ask questions like that, so I just wanted to have a fast Clear up of that. If you have other questions of any type, please reach out to me at Beyond PhD Coaching. I'm always glad to talk to you and would love to be able to help you finish. Good luck.
