Talk:Type I and type II errors

Neeed a better reference for alpha as conventional symbol for Type I error

I'm looking for a reference to support the assertion that Alpha is commonly used as a symbol for type I error. (My primary goal is to add it to Greek_letters_used_in_mathematics,_science,_and_engineering#Concepts_represented_by_a_Greek_letter, but it makes sense to use it as a reference in this article. ) I did see the assertion in this article with a reference at the end of the paragraph. However, while the reference supports the assertion "usually, the significance level is set to 0.05...", and I have edited the reference to include the page number in a supporting quote, it doesn't definitively support the assertion "usually denoted by the Greek letter alpha". I did see the symbol used on page 404 but I don't see any support that it is the usual convention. If someone is aware of a reliable source better supporting the assertion, I'd like to know so that this article can be improved as well as the Greek letter article. S Philbrick(Talk) 15:19, 10 February 2025 (UTC)

Proposed summary for technical prose

I've been using Google's Gemini 2.5 Pro Experimental large language model to create summaries for the most popular articles with {{Technical}} templates. This article, Type I and type II errors, has such a template above the entire article. Here is the paragraph summary at grade 5 reading level which Gemini 2.5 Pro suggested:

Sometimes when we test an idea to see if it's true, we can make a mistake. There are two main kinds of mistakes called errors. A Type I error is a "false positive," which is like thinking something is true when it's really not, such as a fire alarm going off when there is no fire. A Type II error is a "false negative," which means missing something that is actually true, like not finding a hidden toy even though it's there. These mistakes can happen when doctors test for sickness or when computers check for problems. People try hard not to make these errors, but it's hard to avoid both types completely, and trying to make fewer of one type of mistake can sometimes make the other type happen more often.

While I have read and may have made some modifications to that summary, I am not going to add it to the article because I want other editors to review, revise if appropriate, and add it instead. This is an experiment with a few dozen articles initially to see how these suggestions are received, and after a week or two, I will decide how to proceed. Thank you for your consideration. Cramulator (talk) 13:09, 2 April 2025 (UTC)

Use of LLM needs to be discussed by the entire Wikipedia community, not just those interested in one article. You can start at WP:Village pump. Sundayclose (talk) 16:03, 2 April 2025 (UTC)

I am retracting this and the other LLM-generated suggestions due to clear negative consensus at the Village Pump. I will be posting a thorough postmortem report in mid-April to the source code release page. Thanks to all who commented on the suggestions both negatively and positively, and especially to those editors who have manually addressed the overly technical cleanup issue on six, so far, of the 68 articles where suggestions were posted. Cramulator (talk) 01:47, 5 April 2025 (UTC)

Simplification: 'Erroneous' vs. 'Incorrect'

New editor here, so feel free to correct how I'm engaging on the talk page. I wanted to gauge thoughts on changing 'erroneous' to 'incorrect' in the intro section to improve readability. I believe this change would make the article more approachable to a wider audience without meaningfully changing the content. Thanks. Dr.of.Wumbology (talk) 16:27, 21 October 2025 (UTC)

Example Section: Incorrect Interpretation of p-values

The sentence "For example, if the p-value of a test statistic result is 0.0596, then there is a probability of 5.96% that we falsely reject H0 given it is true." is misguiding and represents a common misconception about the observed significance.

This exact phrasing is discussed in (for example) Gigerenzer, G., Krauss, S., & Vitouch, O. (2004). The null ritual: what you always wanted to know about significance testing but were too afraid to ask. The Sage Handbook of Quantitative Methodology for the Social Sciences. Thousand Oaks, CA: Sage, 391-408. ~2026-21988-20 (talk) 07:08, 10 April 2026 (UTC)