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").
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...
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. How Do You...
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 the conclusion of the experiment can be rightly interpreted. Type I Error - False...
Type II error In hypothesis testing, a Type II error occurs when thenull hypothesisis not rejected even though it is false. The probability of committing Type II errors can be reduced by increasing the sample size and the statistical significance....
Antezana, M. A.; Hudson, R. R., 1999: Type I error and the power of the s-test: old lessons from a new, analytically justified statistical test for phylogenies. Syst. Biol. 48, 300-316.Antezana, M. A. & Hudson, R. R. (1999). Type I error and the power of the s-test: ...
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 of errors when the time comes to decide whether or not to reject the null hypothesis...
Unlike a Type I error, a Type II error is not really an error. When a statistical test is not significant, it means that the data do not provide strong evidence that the null hypothesis is false. Lack of significance does not support the conclusion that the null hypothesis is true. ...
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...
statistical tests.i, Average target trial minus control trial dF/Fimage cropped and centred on each photostimulated cell.j,k, Same asi, but for each influenced Sst44 cell (j) and each non-Sst cell (k). Data are mean ± bootstrapped 95% confidence intervals.n = 4 mice, 20 ...