And in this case you would stake an innocent person. Type I error is, "Well this is a vampire when actually it's not a vampire". So we see that there's a possibility that you correctly identify this person as a vampire and stake them. And this person can be a true vampire, or it can just be a random person on the street. You have to make a decision to either stake or not stake this individual. And you have some suspicions that this person might be a vampire, but it could also be someone who's not getting enough sunlight. Let's say you're walking down the street and you come across this person. In the goal of error control, what you try to do is prevent making a fool out of yourself, too often in the long run and I think this is a very useful thing to try when you draw inferences from your data. Now we talk about this in terms of the Type 1 and the Type II errors that you can make. When you say, "Yes, I accept the null hypothesis", or, "I will reject the null hypothesis", controlling the amount of times in the long run that you make a mistake. In the Nymen Pearson approach, the main thing is trying to control the errors when you draw inferences from your data. One of the three approaches to drawing inferences from your data was the path of Action, the Neyman-Pearson approach. If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions" View Syllabus More than 30.000 learners have enrolled so far!
TYPE 1 ERROR HOW TO
Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.Īll videos now have Chinese subtitles. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses.
In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. This course aims to help you to draw better statistical inferences from empirical research.
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