What are the major differences between pratical and statistical significance?
The major differences between practical and statistical significance are as follows:
1. Definition:
- Practical significance: It refers to the significance of an effect or result in terms of its real-world importance or relevance.
- Statistical significance: It refers to the likelihood that an observed effect or result is not due to random chance.
2. Focus:
- Practical significance: It focuses on the magnitude or size of an effect, rather than just determining whether an effect exists or not.
- Statistical significance: It primarily focuses on whether the observed effect is statistically different from what would be expected by chance.
3. Interpretation:
- Practical significance: It involves making subjective judgments about the meaningfulness or importance of an effect in a practical context.
- Statistical significance: It involves making objective judgments based on statistical tests, which provide a measure of the likelihood that an effect is true and not due to chance.
4. Sample size:
- Practical significance: It is generally not heavily influenced by sample size, as it primarily relies on the magnitude of the effect rather than the precision of the estimate.
- Statistical significance: It can be influenced by sample size, as larger sample sizes tend to increase the power of statistical tests, making it easier to detect small but significant effects.
To determine practical significance, you need to consider the context and evaluate the importance of the effect in real-world terms. On the other hand, statistical significance is determined through statistical tests, such as t-tests or chi-square tests, which assess the likelihood of observing the effect under the null hypothesis of no effect. It is important to note that while statistical significance is a necessary condition for practical significance, it is not sufficient – just because an effect is statistically significant does not automatically mean it is practically significant.