Simple stats problem: number of tests to be significant?

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In summary, the conversation discusses how to determine the number of tests needed to be 95% sure of the results for a component being tested. It suggests using the Binomial distribution and the Central Limit Theorem to estimate the mean number of successes and determine a 95% confidence interval.
  • #1
chaoticfarmin
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Hey guys,

Basically I have a component that I want to test multiple times. The outcome of each test is simply a Pass/Fail. What I want to know is how many tests do I have to do to be (say) 95% sure that I have the right result?

My stats knowledge is a bit hazy but I know that for these type of yes/no situations it may be possible to use the Binomial distribution. Am I on the right tracks?

Cheers if you can help.
 
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  • #2
This is an idea; I don't know what the real-life constraints you are working with, so here is a general idea: you can use the CLT to estimate the mean: take many random, independent samples (tests, with results of pass/fail, or 1/0) of the same size N: for each sample compute the mean number of sucesses (total successes/total trials). By the CLT, this random variable, the sampling mean, has a normal distribution. Then use a 95% confidence interval about the samplingn mean.
 

1. What is a "simple stats problem"?

A simple stats problem refers to a statistical scenario that can be solved using basic statistical techniques and does not involve complex mathematical calculations.

2. What does "number of tests to be significant" mean?

The number of tests to be significant refers to the number of statistical tests that need to be conducted in order to obtain a significant result or to reject the null hypothesis.

3. How do you determine the number of tests to be significant?

The number of tests to be significant can be determined by conducting a power analysis, which takes into account factors such as effect size, sample size, and desired level of significance. Alternatively, it can also be determined by using statistical software.

4. Why is it important to know the number of tests to be significant?

Knowing the number of tests to be significant is important because it helps to avoid conducting an excessive number of tests, which can increase the chances of obtaining false positive results. It also ensures that the results are reliable and can be generalized to the larger population.

5. Can the number of tests to be significant vary for different statistical tests?

Yes, the number of tests to be significant can vary for different statistical tests. Some tests may require a larger sample size or a higher level of significance in order to obtain a significant result, while others may require a smaller sample size or a lower level of significance.

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