The p value indicates the probability of observing a difference as large or larger than what was observed, under the null hypothesis. But if the new treatment has an effect of smaller size, a study with a small sample may be underpowered to detect it.
What does p-value of 0.05 mean? If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.
A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true. The lower the p-value, the greater the statistical significance of the observed difference. A p-value of 0.05 or lower is generally considered statistically significant.
If the p-value is less than 0.05, we reject the null hypothesis that there's no difference between the means and conclude that a significant difference does exist. If the p-value is larger than 0.05, we cannot conclude that a significant difference exists.
Since a p-value of 0.045 is less than 10% (0.1) level of significance, there's sufficient evidence to reject the null hypothesis and support the alternative hypothesis.
Given that a p value of . 047 is more precise than . 05, it is safe to say that your value is significant (if you set a cutoff of a=. 05 before your analysis at least).
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.
Any p value less then 0.05 is considered significant in a statistical sense, even if the actual mean difference is quite small. It's rare that someone bothers comparing two p values that are so high (0.2 and 0.9). Both really indicate the same thing: no substantial difference between your two samples.
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.
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.
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.
The p-value is like the strength of the evidence against this defendant. A low p-value is similar to finding clear fingerprints at the scene — it suggests strong evidence against your hypothesis, indicating that your new feature might indeed be making a difference.
The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant.
The level of significance is the probability that the result reported happened by chance. For example, a level of significance of 0.05 means that there is a 5% chance that the result is insignificant, or that it just happened by chance alone.
P values should be given to two significant figures, unless p<0.0001. For p values between 0.001 and 0.20, please report the value to the nearest thousandth. For p values greater than 0.20, please report the value to the nearest hundredth. For p values less than 0.001, report as 'p<0.001'.
A reported P-value of 0 can mean either or both of the P-value being (1) too small to calculate or (2) smaller than the reported resolution. In Stata -- which this question is not about -- P reported as 0.000 means just <0.0005 (and further decimal places can usually be retrieved with some effort).
This makes your results more reliable. 0.05: Indicates a 5% risk of concluding a difference exists when there isn't one. 0.01: Indicates a 1% risk, making it more stringent.
If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
A big t, with a small p-value, means that the null hypothesis is discredited, and we would assert that the means are significantly different in the way specified by the null hypothesis (and a small t, with a big p-value means they are not significantly different in the way specified by the null hypothesis).
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.
Lower p-values represent stronger evidence. Like the significance level, the p-value is stated in terms of the likelihood of your sample evidence if the null is true. For example, a p-value of 0.03 indicates that the sample effect you observe, or more extreme, had a 3% chance of occurring if the null is true.
A p-value of 0.02 means that the measured something is statistically significant at all significance levels of 2% and above. RA Fisher used a significance level of 5% in some of his books and many people have foolishly used 5% for everything they do.
The p-value is 0.026 (from LinRegTTest on your calculator or from computer software). The p-value, 0.026, is less than the significance level of α=0.05.
The P = 0.038 means that there is only a 3.8% chance that this observed difference between the groups occurred by chance (which is less than the traditional cut-off of 5%) and therefore, statistically significant.
