In an April 30, 2015 note in Nature (vol 520, p. 612), Jeffrey Leek and Roger Peng note that p-values get intense scrutiny, while all the decisions that lead up to the p-values get little debate. I wholeheartedly agree, and so I'm creating a Littl...
This deserves to be read, re-read, re-re-read, and taken to heart. The ASA makes their statement on p-values.Some key points:p-values by themselves offer very little information on the utility of a model, the truth of a statement, or what is behind a d...
The first colloquium speaker at this semester, professor Wei Zheng from IUPUI, will give a talk on “Universally optimal designs for two interference models“. In this data explosive age, people are easy to get big data set, which renders people difficult to make inferences from such massive data. Since people usually think that with more […]
The first colloquium speaker at this semester, professor Wei Zheng from IUPUI, will give a talk on “Universally optimal designs for two interference models“. In this data explosive age, people are easy to get big data set, which renders people difficult to make inferences from such massive data. Since people usually think that with more […]
Introduction My statistics education focused a lot on normal linear least-squares regression, and I was even told by a professor in an introductory statistics class that 95% of statistical consulting can be done with knowledge learned up to and including a course in linear regression. Unfortunately, that advice has turned out to vastly underestimate the […]
Recently some papers discussed in our journal club are focused on integrative clustering of multiple omics data sets. I found that they are all originated from factor analysis and make use of the advantage of factor analysis over principal component analysis. Let’s recall the model for factor analysis: where () and , with mean and […]
For a binary classifier, its positive predictive value (PPV) is the proportion of positively classified cases that were truly positive. its negative predictive value (NPV) is the proportion of negatively classified cases that were truly negative. In a later Statistics and Machine Learning Lesson of the Day, I will discuss the differences between PPV/NPV and sensitivity/specificity […]
In an earlier video, I showed how to calculate expected counts in a contingency table using marginal proportions and totals. (Recall that expected counts are needed to conduct hypothesis tests of independence between categorical random variables.) Today, I want to share a second video of calculating expected counts – this time, using joint probabilities. This method uses […]
The Hardy-Weinberg law is a fundamental principle in statistical genetics. If its 7 assumptions are fulfilled, then it predicts that the allelic frequency of a genetic trait will remain constant from generation to generation. In this new video tutorial in my Youtube channel, I explain the math behind the Hardy-Weinberg theorem. In particular, I clarify […]
A common task in statistics and biostatistics is performing hypothesis tests of independence between 2 categorical random variables. The data for such tests are best organized in contingency tables, which allow expected counts to be calculated easily. In this video tutorial in my Youtube channel, I demonstrate how to calculate expected counts using marginal proportions […]
