Statistics is used to differentiate true causal associations from chance-mediated pseudo-causalities. Therefore, a p-value of <0.05 connotes accuracy. Whether the association is significant (relevant), it depends on the description of the numerical difference or the association measures of categorical outcomes.
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.
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.
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.
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.
Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute
What does p-value of 0.01 mean?
A P-value of 0.01 infers, assuming the postulated null hypothesis is correct, any difference seen (or an even bigger “more extreme” difference) in the observed results would occur 1 in 100 (or 1%) of the times a study was repeated.
If a statistical test returns a p-value of 0.07, for example, then this p-value indicates that there would be a 7% chance of obtaining the collected data if the null hypothesis were true.
What if the p-value was 0.2? Then there is a 20% probability that you would see at least a 5 point higher mean score in the reading group given there is no effect of reading.
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 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.
In practice, the smaller the calculated p value, the more we consider the null hypothesis to be improbable; consequently, the smaller the p value, the more we consider the alternative hypothesis to be probable (i.e., that the groups are indeed different) [14].
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.
And this is exactly it: When we put it that way, saying that we want the probability (of the null hypothesis being true) — called a p-value — to be less than 5%, we have essentially set the level of significance at 0.05. If we want the probability to be less than 1%, we have set the level of significance at 0.01.
If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis.
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.
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 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.
Can the p-value be greater than 1? P-value means probability value, which tells you the probability of achieving the result under a certain hypothesis. Since it is a probability, its value ranges between 0 and 1, and it cannot exceed 1.
What is the difference between p-value and significance level?
The p-value represents the strength of evidence against the null hypothesis, while the significance level represents the level of evidence required to reject the null hypothesis. If the p-value is less than the significance level, the null hypothesis is rejected, and the alternative hypothesis is accepted.
Again: A p-value of less than . 05 means that there is less than a 5 percent chance of seeing these results (or more extreme results), in the world where the null hypothesis is true. This sounds nitpicky, but it's critical. It's the misunderstanding that leads people to be unduly confident in p-values.
So, you might get a p-value such as 0.03 (i.e., p = . 03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true.
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.
Keeping the significance threshold at the conventional value of 0.05 would lead to a large number of false-positive results. For example, if 1,000,000 tests are carried out, then 5% of them (that is, 50,000 tests) are expected to lead to p < 0.05 by chance when the null hypothesis is actually true for all these tests.
