How does p-value significance influence your research conclusions?
Understanding p-value significance is crucial in data science research as it helps determine the reliability of your results. When you perform a hypothesis test, you're essentially asking whether the data you have is likely to have occurred under a certain assumption. The p-value, or probability value, is a measure that helps you answer this question. It tells you the likelihood of observing your data, or something more extreme, if the null hypothesis (the default assumption of no effect or no difference) is true. If the p-value is low, typically below a threshold of 0.05, it suggests that your data is unlikely under the null hypothesis, leading you to reject the null and consider your results statistically significant. However, it's important to remember that statistical significance does not necessarily imply practical significance or causality.