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A p-value less than or equal to a predetermined significance level (often 0.05 or 0.01) indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis.
Common choices for significance levels are 0.01, 0.05, and 0.10. If the p-values is less than our significance level, then we can reject the null hypothesis. Otherwise, if the p-value is equal to or greater than our significance level, then we fail to reject the null hypothesis.
The p value, or probability value, tells you the statistical significance of a finding. In most studies, a p value of 0.05 or less is considered statistically significant, but this threshold can also be set higher or lower.
Using the significance level of 0.05, the sample effect is statistically significant. Our data support the alternative hypothesis, which states that the population mean doesn’t equal 260. We can conclude that mean fuel expenditures have increased since last year.
Your results are statistically significant if the p-value is ≤ your significance level. For example, if your p-value is 0.01 and your significance level is 0.05, your results are statistically significant.
Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .
If you conduct a statistical test using a significance level of 0.1, 0.05, or 0.01 (or any significance level greater than 0.000) and get a p-value of 0.000, then reject the null hypothesis. Related. Posted in Programming. A simple explanation of how to interpret a p-value of 0.000.
In research, statistical significance measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer. We can better understand statistical significance if we break apart a study design. [1] [2] [3] [4] [5] [6] [7]
A p-value of 0.05 or lower is generally considered statistically significant. P-value can serve as an alternative to—or in addition to—preselected confidence levels for hypothesis testing....
Because there are clear cut-off values for the p-value for a result to be considered statistically significant (usually p < .05 or p < .01), it supports a black-or-white style of thinking.