P-value
In spatial statistics, a measure used to determine the significance of spatial patterns or relationships.
What does the P-value indicate?
One statistical metric that aids in assessing the significance of findings in hypothesis testing is the P-value. Assuming that the null hypothesis—typically a claim that there is no effect or difference—is correct, it shows the likelihood of getting the observed data, or something more extreme.
Researchers reject the null hypothesis and come to the conclusion that there is a statistically significant effect or association when the P-value is low (usually less than 0.05), indicating that the observed result is unlikely to have been the result of pure chance. On the other hand, a high P-value suggests that there is not enough evidence to support an alternative hypothesis and that the results are consistent with the null hypothesis.
P-values are frequently used in GIS and spatial analysis to evaluate the importance of model findings or spatial trends.
Related Keywords
The p-value in statistics is the likelihood that, under the null hypothesis, findings will be at least as extreme as the observed data. A low p-value (usually less than 0.05) shows strong evidence to reject the null hypothesis, whereas a large p-value implies inadequate evidence to do so. This helps quantify the level of evidence against the null hypothesis.
One statistical metric that aids in assessing the strength of the evidence against a null hypothesis is the p-value. The criterion for determining whether to reject the null hypothesis is the significance level (α), which is frequently set at 0.05. Results are deemed statistically significant if the p-value is smaller than α, which indicates that there is sufficient evidence to imply that the observed effect is unlikely to be the result of chance.
Assuming the null hypothesis is correct, a p-value represents the likelihood of obtaining outcomes as extreme as your observed data. A test statistic (such as t, z, or χ²) is calculated and compared to the appropriate probability distribution to determine it. The null hypothesis is disproved if the p-value falls below a predetermined cutoff point (such as 0.05).
If the null hypothesis is correct, a p-value indicates the likelihood of getting outcomes that are at least as extreme as the ones that were seen. Stronger evidence against the null hypothesis is indicated by a smaller p-value. Typically, the null hypothesis is rejected, indicating that the results are statistically significant, if the p-value is less than a selected significance level (e.g., 0.05).