What are the major differences between practical and statistical significance?
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The height of sucessful business leaders is correlated the level of their position, with those in higher positions having greater height. This is statistically significant. In other words, this relationship is very unlikely to be due to chance.
On the other hand, the differences between levels are merely fractions of an inch, with great overlap between adjacent groups. Because of this, it is very difficult to choose specific individuals to promote on the basis of height. Other variables — most likely also correlated to height — may be influencing this relationship. Thus the height alone would not be a good predictor for choosing an individual for an administrative position. It has little practical significance.
I hope you can relate this example to health care to give you a better understanding. Thanks for asking.
To understand the major differences between practical significance and statistical significance, let's break them down individually:
1. Statistical Significance:
When conducting experiments or analyzing data, statistical significance refers to the likelihood that the results obtained are not due to chance. It helps determine whether an observed difference or relationship between variables is statistically meaningful or not. Statistical significance is typically assessed by conducting hypothesis tests, such as t-tests or chi-square tests, which involve calculating a p-value.
To assess statistical significance, the following steps are typically followed:
1. Define the null hypothesis (H0) and the alternative hypothesis (Ha).
2. Collect and analyze the data.
3. Conduct a hypothesis test and calculate the p-value.
4. Compare the p-value to a predetermined significance level (often denoted as α, commonly set to 0.05 or 0.01).
5. If the p-value is less than the significance level, the result is considered statistically significant, and the null hypothesis is rejected.
It's important to note that statistical significance does not necessarily indicate the practical or real-world importance of the results. It only suggests that the observed difference is unlikely to have occurred by chance.
2. Practical Significance:
Practical significance, also known as clinical or substantive significance, refers to the real-world implications or meaningfulness of the results. It asks the question, "Is the observed difference practically or substantively significant for practical decision-making or practical applications?"
Assessing practical significance involves considering factors such as effect size, practical implications, and context. Here are some important considerations:
1. Effect size: It quantifies the magnitude or strength of the observed difference or relationship between variables. Larger effect sizes generally indicate greater practical significance.
2. Practical implications: It involves interpreting the observed difference in a practical context. Does the difference have a meaningful impact in the real world? Will it affect decision-making or outcomes?
3. Context: Practical significance may vary depending on the field of study or specific objectives. What may be deemed practically significant in one area may not hold the same significance in another.
In summary, statistical significance focuses on whether an observed difference is likely due to chance, while practical significance assesses the real-world implications and importance of the results. Both aspects are essential to consider when evaluating research findings or drawing conclusions.