Statistical significance refers to the claim that a result from data generated by testing or experimentation is not likely to occur randomly or by chance but is instead likely to be attributable to a specific cause.
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.
If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
For example, a p-value that is more than 0.05 is considered statistically significant while a figure that is less than 0.01 is viewed as highly statistically significant.
The significance level is the Type I error rate. So, a lower significance level (e.g., 1%) has, by definition, a lower Type I error rate. And, yes, it is possible to reject at one level, say 5%, and not reject at a lower level (1%).
The p value of 0.006 means that an ARR of 19.6% or more would occur in only 6 in 1000 trials if streptomycin was equally as effective as bed rest. Since the p value is less than 0.05, the results are statistically significant (ie, it is unlikely that streptomycin is ineffective in preventing death).
Common significance levels are 0.10 (1 chance in 10), 0.05 (1 chance in 20), and 0.01 (1 chance in 100). The result of a hypothesis test, as has been seen, is that the null hypothesis is either rejected or not.
A p-value of 0.08 being more than the benchmark of 0.05 indicates non-significance of the test. This means that the null hypothesis cannot be rejected.
The researcher determines the significance level before conducting the experiment. The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
How about 0.051? It's still not statistically significant, and data analysts should not try to pretend otherwise. A p-value is not a negotiation: if p > 0.05, the results are not significant.
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.
The p-value of 0.15, means that the observed difference can be attributed to chance by 15%. In Fisher's approach the null hypothesis is never proved, but is possibly disproved.
The Chi-square test that you apply yields a P value of 0.04, a value that is less than 0.05. You conclude that significantly more patients responded to the antidepressant than to placebo. Your interpretation is that the new antidepressant drug truly has an antidepressant effect.
When power is 50%, a p-value between 0.17-0.18 is just as likely when the alternative hypothesis is true as when the null hypothesis is true (both are again 1% likely to occur). If the power of the test is 50%, a p-value between 0.16-0.17 is 1.1% likely.
|Fisher Significance Testing|
|p = .04 vs. p = .001||p = .001 provides much stronger evidence against the hypothesis than does p = .04.|
|Conclusion||The conclusions of the experiment should not be based on the P-value alone.|
2.2e-16 is the scientific notation of 0.00000000000000022, meaning it is very close to zero. Your statistical software probably uses this notation automatically for very small numbers.
A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference.
Related indices. The E-value corresponds to the expected number of times in multiple testing that one expects to obtain a test statistic at least as extreme as the one that was actually observed if one assumes that the null hypothesis is true. The E-value is the product of the number of tests and the p-value.
Article. The p-value can be perceived as an oracle that judges our results. If the p-value is 0.05 or lower, the result is trumpeted as significant, but if it is higher than 0.05, the result is non-significant and tends to be passed over in silence.
Conventionally, p < 0.05 is referred as statistically significant and p < 0.001 as statistically highly significant.
eg the p-value = 0.01, it means if you reproduced the experiment (with the same conditions) 100 times, and assuming the null hypothesis is true, you would see the results only 1 time. OR in the case that the null hypothesis is true, there's only a 1% chance of seeing the results.
All statistical symbols that are not Greek letters should be italicized (M, SD, N, t, p, etc.). When reporting a significant difference between two conditions, indicate the direction of this difference, i.e. which condition was more/less/higher/lower than the other condition(s).