ASA Connect

Sorry, wrong number: Statistical benchmark comes under fire

Sorry, wrong number: Statistical benchmark comes under fire

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire

Sharing here what I wrote on LinkedIn, which is a good channel for sharing these concerns with a wider professional but non-statistical community. 

A good article on the perils of p-values, written in a clear, accessible manner. This is a good article to share with non-statisticians who need to use statistical results - business managers, engineers, physicians and other medical technicians, attorneys and other legal professionals, and so on. The article is excellent, but I could wish it added one more thing more: P-Hacking. In the article, a study is deemed a failure because it just missed an arbitrary significance level of 0.05. One risk we find in these cases - something people should watch out for - is repeating experiments or other tests over and over until one, by chance, barely achieves the 0.05 level, which is unwisely regarded as a gold standard.

My colleague Eric Vance mentioned the xkcd cartoon on p-hacking (jelly beans and acne) as good explanation of p-hacking for people who aren't statistical experts. 

Sorry, wrong number: Statistical benchmark comes under fire