The p-value represents the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true. A p-value of less than 0.05 is typically considered statistically significant and indicates that the null hypothesis should be rejected.
A p-value greater than 0.05 indicates that the observed results are likely to occur by chance and that the null hypothesis cannot be rejected. This suggests that there may not be a significant relationship between the variables being studied.
No, a high p-value does not necessarily mean that there is no effect. It simply means that there is not enough evidence to reject the null hypothesis. Other factors, such as sample size and study design, should also be considered when interpreting the results.
A larger sample size generally results in a smaller p-value, as it provides more precise estimates of the true effect. This is because a larger sample size reduces the variability in the data and increases the power of the study. However, the exact relationship between sample size and p-value can vary depending on the study design and other factors.
Not necessarily. A low p-value can indicate a significant result, but it does not guarantee that the observed effect is meaningful or that it will generalize to a larger population. Other factors, such as effect size and study design, should also be considered when evaluating the significance of a result.