pvalue

A guide for selecting the level of significancePhoto by Burst on UnsplashIn hypothesis testing, the “p-value < α” is almost universally used as a criterion for statistical significance and also as a decision rule, where α is the level of significance. For example, in testing forH0: θ = 0; H1: θ ≠ 0,where θ is the parameter of interest that represents an effect, we reject H0 of no effect, at the α level of significance, if p-value < α. It is a convention to set α = 0.05, while 0.10 and 0.01 are also widely adopted.…

Better approaches to making statistical decisionsPhoto by Rommel Davila on UnsplashIn establishing statistical significance, the p-value criterion is almost universally used. The criterion is to reject the null hypothesis (H0) in favour of the alternative (H1), when the p-value is less than the level of significance (α). The conventional values for this decision threshold include 0.05, 0.10, and 0.01.By definition, the p-value measures how compatible the sample information is with H0: i.e., P(D|H0), the probability or…

Understand the idea behind the hypothesis testing framework through examplesGreat products aren’t built overnight, rather they’re refined and polished through years of iteration. The most successful teams follow a feedback loop while developing a product. First, they develop an idea, deploy it to production, and monitor the process. Then, based on the data collected, they analyse and determine whether it’s successful. The insights gained in the analysis inform the next iteration of development. François Chollet, the…

A refresher on how to interpret statistical inference correctlyPhoto by Scott Graham on UnsplashStatistical inference, such as hypothesis testing and confidence interval, are well-known concepts in statistics. However, many people including myself would sometimes misinterpret the result from statistical inference. Hopefully, this article would serve as a refresher on how to interpret statistical inference correctly.What is Statistical Inference?If we are studying the average household income in Los Angeles, it is…

Why and how to use forest plots efficiently?From regression to forest plot, image by the authorHumans like to put things into bins in a simple way: Statistically significant or not statistically significant. This dichotomous view of statistical results must stop! For almost a decade, I heard the same debate. On one hand, people argued that statistical significance has the advantage of simplicity and clarity (and many people are already struggling to achieve this). On the other hand, there were those who argued for more…

Communicating Results of Your Statistical AnalysisPhoto by el pepe on UnsplashThe journey of an analyst in the world of data science, fundamentally, consists of three phases: learning, doing, and presenting. These phases are not necessarily linear; of course, most of us go back and forth. Which of these three phases do you perceive to be the most difficult one?Personally, I think the third one is the most complicated. Surely the earlier two phases have enough challenges; however, after a while, you realize that learning…