Hypothesis Testing: Distinguishing Zero vs Practical Equivalence

In summary, a hypothesis test is a statistical method used to determine the likelihood of a specific hypothesis being true in a larger population. The null hypothesis is typically set to zero and practical equivalence refers to a difference that is not statistically significant but still has practical implications. A result is considered statistically significant if the p-value is less than a predetermined significance level. Distinguishing between zero and practical equivalence allows for not only determining statistical significance, but also practical meaningfulness. This is important for decision-making and application of results. The power of a hypothesis test is related to distinguishing zero from practical equivalence as it ensures the test can accurately detect meaningful differences rather than just statistically significant ones.
  • #1
engineer23
75
0
What is the difference between NOT being able to reject the hypothesis that a particular parameter is zero and being able to conclude that it is within some acceptable distance from zero ("practical equivalence")?

I guess this is more of a logic question, but I'm still having trouble understanding this distinction.
 
Physics news on Phys.org
  • #2
One of them says that the value is not zero, the other says it is. In hypothesis testing you never accept the null hypothesis, you only fail to reject it.

Is this what you were after?
 

1. What is a hypothesis test?

A hypothesis test is a statistical method used to determine the likelihood of a specific hypothesis being true in a larger population. It involves collecting and analyzing data to either support or reject a hypothesis.

2. What is the difference between zero and practical equivalence?

In hypothesis testing, the null hypothesis is typically set to zero, indicating no difference or effect between groups. Practical equivalence refers to a difference that is not statistically significant, but still has practical or meaningful implications.

3. How do you determine if a result is statistically significant?

A result is considered statistically significant if the p-value, which is the probability of obtaining the observed result by chance, is less than a predetermined significance level. This level is typically set at 0.05 or 0.01.

4. What is the purpose of distinguishing zero from practical equivalence in hypothesis testing?

Distinguishing between zero and practical equivalence allows researchers to not only determine if there is a statistically significant difference between groups, but also if the difference is practically meaningful. This can help inform decision-making and the application of the results.

5. How is the power of a hypothesis test related to distinguishing zero from practical equivalence?

The power of a hypothesis test refers to its ability to detect a true difference or effect between groups. By distinguishing zero from practical equivalence, researchers can ensure that their hypothesis test has enough power to accurately detect a meaningful difference, rather than just a statistically significant one.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
20
Views
3K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
3K
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
2K
  • Other Physics Topics
Replies
14
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Art, Music, History, and Linguistics
Replies
3
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
15
Views
2K
Replies
26
Views
1K
Back
Top