{"id":176233,"date":"2024-04-03T04:08:00","date_gmt":"2024-04-03T04:08:00","guid":{"rendered":"https:\/\/businessyield.com\/?p=176233"},"modified":"2024-04-29T16:03:24","modified_gmt":"2024-04-29T16:03:24","slug":"statistical-significance","status":"publish","type":"post","link":"https:\/\/businessyield.com\/project-management\/statistical-significance\/","title":{"rendered":"Leveraging Statistical Significance for Business Insights: From Data to Action","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
One summer, I was drinking coffee with a friend who is a medical researcher. We were deep in discussion about a new cancer treatment that had recently made headlines. It was expected to be a game changer, with studies indicating that it greatly improved patient outcomes. Everyone was thrilled\u2014doctors, patients, and even my friend’s colleagues. But my friend was not completely convinced. “Sure, it’s statistically significant,” he added, “but there’s more to it than just a low p-value.” <\/p>
His comments grabbed my attention. I had heard the term “statistically significant” before, but his doubt made me realize that I may not have understood it correctly. As he talked, I realized that statistical significance is only one aspect of the greater picture. It can inform you if something is likely true, but it does not necessarily imply that it is significant.\u00a0<\/p>
This conversation inspired me to learn more about statistical significance, its importance, and how it is applied in research. Along the way, I realized that statistical significance is important for identifying whether a result is likely attributable to chance, but it is not a cure-all. Certain nuances and problems can make or break a study’s credibility.\u00a0<\/p>
In this article, I’ll share what I’ve learned about statistical significance, including its definition, why it’s important, how it’s calculated, and strategies for ensuring accuracy. I will also discuss some common misconceptions. By the end of this article, I believe you will have a better knowledge of statistical significance and how to apply it in real-world research and decision-making. Let’s dive in.<\/p>
Statistical significance is a concept in statistics that determines whether the results of a study or experiment are likely to be true and not simply the result of chance. In layman’s terms, it assists researchers in determining whether a given finding is significant or if it occurred by chance.<\/p>
The p-value, which reflects the likelihood that the observed result would occur if there were no actual effect or difference (the “null hypothesis”), is the most commonly used statistical significance metric. A p-value of 0.05 is a universally accepted level of statistical significance. If the p-value is less than this level, the result is regarded as statistically significant, implying a low likelihood that it occurred by chance.<\/p>
The two principal components of this are its sample size and effect size. This means that, if you reach a certain level of confidence in the outcome of statistical hypothesis testing, the conclusion of a data set may be deemed statistically significant.<\/p>