User:Happyboi2489/sandbox

Paragraph: Set the style of your text. For example, make a header or plain paragraph text. You can also use it to offset block quotes.
 A : Highlight your text, then click here to format it with bold, italics, etc. The “More” options allows you to underline (U), cross-out text ( S ), add code snippets ( { } ), change language keyboards (Aあ), and clear all formatting . 

Links: Highlight text and push this button to make it a link. The Visual Editor will automatically suggest related Wikipedia articles for that word or phrase. This is a great way to connect your article to more Wikipedia content. You only have to link important words once, usually during the first time they appear. If you want to link to pages outside of Wikipedia (for an “external links” section, for example) click on the “External link” tab.

Cite: The citation tool in the Visual Editor helps format your citations. You can simply paste a DOI or URL, and the Visual Editor will try to sort out all of the fields you need. Be sure to review it, however, and apply missing fields manually (if you know them). You can also add books, journals, news, and websites manually. That opens up a quick guide for inputting your citations. Once you've added a source, you can click the “re-use” tab to cite it again.


 * Bullets: To add bullet points or a numbered list, click here.

Insert: This tab lets you add media, images, or tables.

Ω: This tab allows you to add special characters, such as those found in non-English words, scientific notation, and a handful of language extensions.ѾѿҀѥѦѩ

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Akaike information criterion (=AIC) or Bayesian information criterion (=BIC).

A statistical hypothesis test is a method of statistical inference used to determine a possible conclusion from two different, and likely conflicting, hypotheses. In a statistical hypothesis test, a null hypothesis and an alternative hypothesis is proposed for the probability distribution of the data. The comparison of the two models is deemed statistically significant if, according to a threshold probability—the significance level—the data would be unlikely to occur if the null hypothesis were true. A hypothesis test specifies which outcomes of a study may lead to a rejection of the null hypothesis at a pre-specified significance level, while using a pre-chosen measure of deviation from that hypothesis (the test statistic, or goodness-of-fit measure).

The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by considering two types of errors. A Type I error occurs when a true null hypothesis is rejected. A Type II error occurs when a false null hypothesis is not rejected. The pre-chosen significance level is the maximal allowed "false positive rate". One wants to control the risk of incorrectly rejecting a true null hypothesis.

Hypothesis tests based on statistical significance are another way of expressing confidence intervals (more precisely, confidence sets). In other words, every hypothesis test based on significance can be obtained via a confidence interval, and every confidence interval can be obtained via a hypothesis test based on significance.