1. 一类错误（Type I Error）：一类错误指的是在实际情况下，原假设（Null Hypothesis，通常表示无效果或无关联）为真，但是经过假设检验得出拒绝原假设的结论。换句话说，一类错误是错误地拒绝了一个实际上是真实的假设。2. 二类错误（Type II Error）：二类错误指的是在实际情况下，备择假设（Alterna...
In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is the failure to reject a false null hypothesis (a "false negative").
Statistical tests contain experimental errors that can be classified as either Type-I or Type-II errors. It is important to study both these effects in order to be able to manage error and report it, so that theconclusionof the experiment can be rightly interpreted. ...
a Type I error is preferable to a Type II error, but in other applications, a Type I error is more dangerous to make than a Type II error. In order to ensure proper planning for the statistical testing procedure, one must carefully consider the consequences of both of these types...
Type I and type II errors occur during statistical hypothesis testing. While the type I error (a false positive) rejects a null hypothesis when it is, in fact, correct, the type II error (a false negative) fails to reject a false null hypothesis. For example, a type I error would conv...
fire and does not ring in the absence of a fire. However, if the alarm rings when there is no fire, it is a false positive, or a Type I error in statistical terms. Conversely, if the fire alarm fails to ring when there is a fire, it is a false negative, or a Type II error....
A type II error is commonly caused if the statistical power of a test is too low. The higher the statistical power, the greater the chance of avoiding an error. It’s often recommended that the statistical power should be set to at least 80% prior to conducting any testing. ...
1996: Type I error rates and statistical power for the James Second-Order Test and the Univariate F Test in two-way fixed-effects ANOVA models under heteroscedasticity and/or nonnormality. J. Exp. Educ. 65, 57--71.Hsiung, T., & Olejnik, S. (1994, April). Type I error rates and ...
Statistical hypothesis testing implies that no test is ever 100% certain: that’s becausewe rely on probabilities to experiment. When online marketers and scientists run hypothesis tests, they’re both looking forstatistically relevant results. This means that the results of their tests have to be...
, and provides for statistical decisions based on the a priori consideration of the development and environmental costs of Type I and Type II errors. ... BD Mapstone - 《Detecting Ecological Impacts》 被引量: 1164发表: 1995年 Hypothesis testing, type I and type II errors Hypothesis testing ...