A p value of 0.06 means that there is a probability of 6% of obtaining that result by chance when the treatment has no real effect. Because we set the significance level at 5%, the null hypothesis should not be rejected.
A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
Mathematical probabilities like p-values range from 0 (no chance) to 1 (absolute certainty). So 0.5 means a 50 per cent chance and 0.05 means a 5 per cent chance. In most sciences, results yielding a p-value of . 05 are considered on the borderline of statistical significance.
In this case, the p-value is 0.063 and the significance level is 0.05. Since the p-value is greater than the significance level, we fail to reject the null hypothesis and conclude that there is not enough evidence to support the alternative hypothesis.
It is inappropriate to interpret a p value of, say, 0.06, as a trend towards a difference. A p value of 0.06 means that there is a probability of 6% of obtaining that result by chance when the treatment has no real effect. Because we set the significance level at 5%, the null hypothesis should not be rejected.
With more variables and more comparisons, p-values are usually considered significant at values smaller than 0.05 (often 0.01) to avoid risk of Type-I error. At no time is 0.6 (or even the much closer-to-significant 0.06) ever considered significant.
Thus, there is evidence to reject the null hypothesis. On the other hand if the p value was <0.65 then assuming the null hypothesis is true, you would expect to obtain the observed result or more extreme 65% of the time.
The p-value only tells you how likely the data you have observed is to have occurred under the null hypothesis. If the p-value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.
If the p-value is less than 0.05, it is judged as “significant,” and if the p-value is greater than 0.05, it is judged as “not significant.” However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05.
The smaller the p-value the greater the discrepancy: “If p is between 0.1 and 0.9, there is certainly no reason to suspect the hypothesis tested, but if it is below 0.02, it strongly indicates that the hypothesis fails to account for the entire facts. We should not be off- track if we draw a conventional line at 0.05”.
A P-value less than 0.05 is deemed to be statistically significant, meaning the null hypothesis should be rejected in such a case. A P-Value greater than 0.05 is not considered to be statistically significant, meaning the null hypothesis should not be rejected.
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.
For example, a P-value of 0.08, albeit not significant, does not mean 'nil'. There is still an 8% chance that the null hypothesis is true. A P-value alone cannot be used to accept or reject the null hypothesis.
These are as follows: if the P value is 0.05, the null hypothesis has a 5% chance of being true; a nonsignificant P value means that (for example) there is no difference between groups; a statistically significant finding (P is below a predetermined threshold) is clinically important; studies that yield P values on ...
Fisher did not stop there but graded the strength of evidence against null hypothesis. He proposed “if P is between 0.1 and 0.9 there is certainly no reason to suspect the hypothesis tested. If it's below 0.02 it is strongly indicated that the hypothesis fails to account for the whole of the facts.
A p-value of 0.99 means that practically there is no effect, no association, no correlation between two variables and the situation is so simply straight forward that one would not even have to go for any test.
High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it's possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.
'P=0.06' and 'P=0.6' can both get reported as 'P=NS', but 0.06 is only just above the conventional cut-off of 0.05 and indicates that there is some evidence for an effect, albeit rather weak evidence. A P value equal to 0.6, which is ten times bigger, indicates that there is very little evidence indeed.
A small p-value means that it's greater than chance alone, something happened; the test is significant. Whereas a large p-value indicates that the result is within chance or normal sampling error, or in other words nothing happened, the test is not significant. And p values range from 0 to 1.
A p-value of 0.05 is quite a high benchmark which for many studies is appropriate, however, when you're analysing data and making a decision based on statistical probability and the risk of mis-analysis doesn't have severe consequences a p-value of . 2 is quite acceptable.
The P-value is the bottom line of most statistical tests. It is simply the probability that the hypothesis being tested is true. So if a P-value is given as 0.06, that indicates that the hypothesis has a 6% chance of being true.
a certain trend toward significance (p=0.08) approached the borderline of significance (p=0.07) at the margin of statistical significance (p<0.07) close to being statistically significant (p=0.055)
Most authors refer to statistically significant as P < 0.05 and statistically highly significant as P < 0.001 (less than one in a thousand chance of being wrong).
