Comparing subsample distribution vs whole sample

In summary, the individual is looking for a way to remove measurements without replicates in order to use modeling techniques that require mean and standard deviation data. They also want to know what kind of test can be used to show that a subsample of ~100 measurements has the same distribution as the whole sample, despite the data not being independent groups.
  • #1
labrookie
5
0
Hello,

I have sample of ~280 measurements. Some are replicates and some are not. I want to remove the measurements that have no replicates, so that I can apply various modeling techniques that need mean/stdev data.

I would like to show that my subsample of ~100 measurements statistically has the same distribution as the whole sample.

What kind of test can I use for this? I believe my difficulty here is that they are not independent groups of data.

Thank you.
 
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  • #2
labrookie said:
I believe my difficulty here is that they are not independent groups of data.

I believe your difficulty in getting a reply is due to the fact that no one can figure out what you mean by a "replicate".
 

Related to Comparing subsample distribution vs whole sample

1. What is the difference between comparing subsample distribution and whole sample distribution?

The main difference between comparing subsample distribution and whole sample distribution is the size of the sample being analyzed. A subsample is a smaller subset of the overall sample, while the whole sample includes all of the data. This can impact the results of the analysis, as a smaller sample may not be as representative of the entire population as the whole sample would be.

2. Why would someone choose to compare subsample distribution instead of the whole sample distribution?

There are several reasons why someone may choose to compare subsample distribution instead of the whole sample distribution. One reason could be that the data set is very large, making it more efficient to analyze a smaller subset of the data. Another reason could be that the researcher is interested in a specific subpopulation within the larger sample. Additionally, comparing subsample distribution can also help to identify any potential biases or anomalies within the data.

3. What are the potential drawbacks of comparing subsample distribution?

One potential drawback of comparing subsample distribution is that the results may not be as representative of the entire population as the whole sample distribution. This could lead to biased or inaccurate conclusions if the subsample is not chosen carefully. Additionally, subsamples may also introduce random errors or variability due to the smaller size of the sample.

4. How can one ensure the validity of comparing subsample distribution?

To ensure the validity of comparing subsample distribution, it is important to carefully select the subsample in a way that is representative of the entire population. This can be done through random sampling, where each member of the population has an equal chance of being selected for the subsample. Additionally, the subsample should be large enough to minimize any potential random errors or variability.

5. What statistical tests can be used to compare subsample distribution vs whole sample distribution?

There are several statistical tests that can be used to compare subsample distribution vs whole sample distribution, depending on the type of data being analyzed and the research question being addressed. Some common tests include t-tests, ANOVA, and chi-square tests. It is important to select the appropriate test based on the specific research question and data set in order to obtain accurate and meaningful results.

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