What are the major differences between practical and statistical significance?
Statistical significance is that the results are very unlikely due to chance.
Practical significance is the ability to use that information in real-life situations.
For example, those who hold higher positions in companies are significantly taller (statistically) than those in lower status positions. However, the height differences between various levels are so small, they cannot be used as one criterion for giving promotions.
I searched Google under the key words "statistical practical significance" to get these possible sources:
http://www.abacon.com/graziano/ch05/sld024.htm
http://wiki.answers.com/Q/What_is_practical_significance
http://www.helium.com/items/886594-using-practical-and-statistical-significance-in-testing-the-null-hypothesis
In the future, you can find the information you desire more quickly, if you use appropriate key words to do your own search. Also see http://hanlib.sou.edu/searchtools/.
I hope this helps. Thanks for asking.
Practical significance and statistical significance are two distinct concepts used in data analysis, especially in the field of statistics. Understanding the differences between them is essential in interpreting research findings.
Practical significance refers to the real-world importance or meaningfulness of a research finding. It focuses on the extent to which a result has practical implications or if it is relevant to a specific context or problem. Determining practical significance often involves considering the magnitude or size of an effect, the potential impact on decision-making, and the relevance to the target population. However, determining practical significance is subjective and can vary depending on the specific field or domain.
Statistical significance, on the other hand, refers to the likelihood that an observed result is not due to chance alone. It is a measure of the confidence we can have in generalizing the results to a larger population. Statistical significance is typically determined using statistical tests, such as the t-test or chi-square test, which calculate a p-value. If the p-value is below a predetermined threshold (often 0.05), the result is considered statistically significant, implying that the observed effect is unlikely to have occurred by chance alone.
To differentiate between practical and statistical significance, it can be helpful to think of statistical significance as a measure of confidence in the existence of an effect, while practical significance assesses the importance or relevance of that effect in the real world. A finding can be statistically significant without being practically significant if the observed effect size is small or lacks practical implications. Similarly, a finding can be practically significant without being statistically significant if the effect size is large but fails to meet the predetermined threshold for statistical significance.
In summary, practical significance focuses on the real-world importance or relevance of a finding, whereas statistical significance measures the likelihood that the observed result is not due to chance alone. Both concepts play a crucial role in data analysis and should be considered together when interpreting research findings.