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.
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.
P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative 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.
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).
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.
As mentioned above, only two p values, 0.05, which corresponds to a 95% confidence for the decision made or 0.01, which corresponds a 99% confidence, were used before the advent of the computer software in setting a Type I error.
If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
If p values are reported, follow standard conventions for decimal places: for p values less than 0.001, report as 'p<0.001'; for p values between 0.001 and 0.01, report the value to the nearest thousandth; for p values greater than or equal to 0.01, report the value to the nearest hundredth; and for p values greater ...
You can reject a null hypothesis when a p-value is less than or equal to your significance level. The p-value represents the measure of the probability that a certain event would have occurred by random chance. You can calculate p-values based on your data by using the assumption that the null hypothesis is true.
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 defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance.
A high P-value, between 0.5 and 1.0, means that it is more likely that the results occurred by random chance, or that the difference is not statistically significant in the case of a hypothesis test.
The p-value obtained from the data is judged against the alpha. If alpha=0.05 and p=0.03, then statistical significance is achieved. If alpha=0.01, and p=0.03, statistical significance is not achieved.
The meaning of a p-value is “The probability of observing a test statistic at least as extreme as the one you have, if the null hypothesis is true.” Therefore, a p-value of 0 means that if you have observed this test statistic, the null hypothesis cannot be true. In practice, such a thing doesn't happen.
What P = 1.00 means is that if the null hypothesis is true and if we perform the study in an identical manner a large number of times, then on 100% of occasions we will obtain a difference between groups of 0% or greater!
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.
While a p-value can be extremely small, it cannot technically be absolute zero. When a p-value is reported as p = 0.000, the actual p-value is too small for the software to display. This is often interpreted as strong evidence against the null hypothesis. For p values less than 0.001, report as p < .001.
Context: A p-value of 0.01 indicates that the observed result is statistically significant, meaning it is unlikely to have occurred by random chance alone. In summary, a p-value of 0.01 indicates strong evidence against the null hypothesis, suggesting that the observed results are statistically significant.
This leads to the guidelines of p<0.001 indicating very strong evidence, p<0.01 strong evidence, p<0.05 moderate evidence, p<0.1 weak evidence or a trend, and p≥0.1 indicating insufficient evidence.
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.
If the p-value is larger than 0.05, we cannot conclude that a significant difference exists. That's pretty straightforward, right? Below 0.05, significant. Over 0.05, not significant.
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”.
