Calculating P-value comparing two percentages of accuracy?

In summary, the speaker is a researcher comparing two methods of microbial identification and is struggling to remember which method to use to calculate the p-value for comparing percentages. They mention trying to apply chi-squared and suggest looking into paired and un-paired observations, as well as the t-test for equality of means.
  • #1
phys-lexic
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I have been out of unversity statistics and biometry for a few years do date. I am working in a clical research facility and we are comparing two methods of microbial identification and am now working on the results / charts / statistics. Forgive me for my ignorance, but I am forgetting which method would I use to calculate the p-value for comparing percentages; i.e. out of 100 samples method 1 was 90% accurate and method 2 was 60% accurate (random values). I was going through my old notes and tried to apply chi-squared, etc. It seems I didn't retain much.

Thanks in advance.
 
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  • #2
You haven't described a specific situation. For example, were both methods applied to each of 100 samples? Or was one method applied to 100 samples and the other method applied to 100 different samples?

Perhaps your old notes deal with "paired" and "un-paired" observations. Is the accuracy of an application of a method to a single sample a result like 0.93? For un-paired observations, look up the "t-test for equality of means".
 

Related to Calculating P-value comparing two percentages of accuracy?

1. What is a P-value and why is it important in comparing two percentages of accuracy?

A P-value is a statistical measure that helps determine the likelihood of obtaining a specific result by chance alone. In the context of comparing two percentages of accuracy, the P-value reflects the probability of observing the difference in accuracy between the two groups if there is truly no difference between them. It is important because it allows us to determine whether the observed difference is statistically significant or simply due to chance.

2. How is a P-value calculated when comparing two percentages of accuracy?

The P-value is calculated using a statistical test, such as the chi-square test or the t-test, which takes into account the sample sizes and the difference in percentages between the two groups. The resulting value is then compared to a predetermined significance level, usually 0.05, to determine whether the difference in accuracy is statistically significant.

3. Can a P-value be negative when comparing two percentages of accuracy?

No, a P-value cannot be negative. It represents a probability and therefore must fall between 0 and 1. A P-value close to 0 indicates that the observed difference in accuracy is not likely due to chance, while a P-value close to 1 suggests that the difference is likely due to chance.

4. What does it mean if the P-value is greater than the significance level?

If the P-value is greater than the significance level, it means that the observed difference in accuracy between the two groups is not statistically significant. In other words, there is not enough evidence to conclude that the difference is not due to chance. This does not necessarily mean that there is no difference between the two groups, but rather that the difference is not large enough to be considered significant.

5. How can the P-value be used to make a decision about whether to accept or reject the null hypothesis?

The P-value can be compared to the significance level to make a decision about whether to accept or reject the null hypothesis. If the P-value is less than or equal to the significance level, typically 0.05, then the null hypothesis can be rejected and it can be concluded that the difference in accuracy between the two groups is statistically significant. If the P-value is greater than the significance level, then the null hypothesis cannot be rejected and it cannot be concluded that the difference is statistically significant.

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