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What happens when you lower the significance level?
Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant.
What does a low P value prove?
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
What is wrong with significance testing?
The most common misinterpretation is that the P value is the probability that the null hypothesis is true, so that a significant result means that the null hypothesis is very unlikely to be true. Because the type II error rate is 50% (second assumption) we reject the null hypothesis in 50 of these 100 studies.
What does P-value 0.05 mean?
P > 0.05 is the probability that the null hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.
Why can’t you use a significance level of 0 %? Doesn t this mean there is no chance of a Type I error?
If the significance level is 0%, then no P-value will ever be small enough, since P-values can’t be zero. Then you will never reject the null hypothesis, even when it’s wrong, making your hypothesis tests pretty useless.) So, you can lower α to reduce the chance of a Type I error.
What does P value of 0.008 mean?
A P value of 0.008 indicates that the probability of observing a 4-day difference in treatment duration be- tween the 2 bracket systems, when in reality no differ- ence exists (H0 is true), is very low (8 in 1000). Therefore, this difference is unlikely to be due to chance alone; thus, we reject the Ho.