The p-value in a regression model measures the strength of evidence against the null hypothesis, indicating whether the observed data could occur by chance. A low p-value (<0.05) suggests that the coefficient is statistically significant, implying a meaningful association between the variable and the response.
A P-value less than 0.5 is statistically significant, while a value higher than 0.5 indicates the null hypothesis is true; hence it is not statistically significant.
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). The asterisk system avoids the woolly term "significant".
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.
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.
Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute
Is 0.05 or 0.01 p-value better?
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.
And although 0.5 or below is generally regarded as the threshold for significant results, that doesn't always mean that a test result which falls between 0.05 and 0.1 isn't worth looking at. It just means that the evidence against the null hypothesis is weak.
If the p-value comes in at 0.2, you'll stick with your current campaign or explore other options. But even if it had a significance level of 0.03, the result is likely real, though quite small. In this case, your decision probably will be based on other factors, such as the cost of implementing the new campaign.
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 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.
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. So what is the p-value really, and why is 0.05 so important?
In reality, p value can never be zero. Any data collected for some study are certain to be suffered from error at least due to chance (random) cause. Accordingly, for any set of data, it is certain not to obtain "0" p value. However, p value can be very small in some cases.
A P-value of 0.000 indicates a highly significant result, meaning that the observed data would be extremely unlikely under the null hypothesis. However, it's essential to report P-values accurately and understand their context within your study.
A p-value of 0.001 is highly statistically significant beyond the commonly used 0.05 threshold. It indicates strong evidence of a real effect or difference, rather than just random variation.
'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.
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.
A P-value above 0.5 is considered to be insignificant while anything below 0.05 is considered to be significant and a P-value less than 0.001 is extremely significant.
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.
In other words, the lower the p-value, the less compatible the data is to the null hypothesis (i.e. despite both being significant, p = 0.04 is a weaker significance value than p = 0.004 and therefore we would be more confident that the results are 'true' with p = 0.004) if we are confident that all assumptions were ...
However, defining what constitutes a small p-value is not straightforward1. Commonly adopted guidelines suggest p < 0.001 as very strong evidence, p < 0.01 as strong evidence, p < 0.05 as moderate evidence, p < 0.1 as weak evidence or a trend, and p ≥ 0.1 as insufficient evidence.
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.
So, if P-value is greater than the 0.20 level of significance for statistically significant then it will also be greater than the 0.05 level of significance. At the 0.05 level of significance, the result will be statistically significant.
A low p-value (usually <0.05) indicates that the observed result is unlikely to be due to chance alone and provides evidence against the null hypothesis - in other words, that your result is more likely to be due to a real phenomenon rather than chance.
A lower significance level (e.g., 0.01) reduces the risk of false positives but may require larger sample sizes. A higher level (e.g., 0.1) allows for faster decisions but increases the chance of false positives. In practice, the choice of significance level often depends on the business context and the cost of errors.
We can work out the chances of the result we have obtained happening by chance. If a p-value reported from a t test is less than 0.05, then that result is said to be statistically significant. If a p-value is greater than 0.05, then the result is insignificant.
