# Estimating Gene Mutation Proportion: A & B Approaches

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• schniefen
In summary: Therefore, caution is warranted.Summary: In summary, both approaches A and B may not provide a random sample of the population, leading to biased results. Self-selecting samples can introduce dependence and bias in the estimation process. Caution is recommended when using these approaches for estimation.
schniefen
TL;DR Summary
Two approaches in estimating the proportion of the population with a certain gene mutation.
The proportion of individuals that carry a certain gene mutation in the population is unknown. A research assistant at a medical laboratory wants to estimate this proportion. The research assistant is thinking of two approaches:

A. Take blood samples from all individuals that come to the hospitals ER ward during a month, and test their gene status.

B. Sample people, by reaching them by cell phone or land line phone, and ask them to participate and have their gene status tested.

Suppose in both cases A. and B., all individuals that are asked will agree to have their blood sample taken and gene tested. Can you think of features in approaches A. and B. that may not fit into our setup for estimation?

Since the information in the problem is quite general, it is hard to make any definite conclusion about each approach. For instance, if one would like to use an MLE, one would like the sample to be i.i.d., however, this could probably be accomplished in both approaches. Maybe in A. there may be some issue with independence, since imagine a diseases that causes gene mutation and easily spreads, then every person that gets tested on the day when a person with that disease was present in the ward will likely turn out to also have a gene mutation.

schniefen said:
Summary:: Two approaches in estimating the proportion of the population with a certain gene mutation.

Can you think of features in approaches A. and B. that may not fit into our setup for estimation?
The main issue is that A will not be a random sample of the population.

schniefen
Both of these are, at least partially, what are called "self-selecting samples". They have done something that makes them more likely to end up in your selected sample. This is only safe if their actions are independent of the property that you want to test for. If certain gene mutations tend to put more people in the ER, then your results are biased. Likewise, if the gene mutation tends to influence whether they have a phone, your results are biased. I can think of situations where that can occur in either case, so it is best for you to consider it. This is a very treacherous subject and it is easy to overlook dependencies that will bias your results.

Last edited:
Klystron, jim mcnamara and Dale

## 1. What is the purpose of estimating gene mutation proportion?

The purpose of estimating gene mutation proportion is to determine the frequency of genetic mutations within a population. This information is important for understanding the prevalence of certain genetic disorders and for identifying potential risk factors for these disorders.

## 2. What are the A and B approaches for estimating gene mutation proportion?

The A and B approaches are two methods used to estimate gene mutation proportion. The A approach involves directly counting the number of individuals with a specific mutation in a sample population. The B approach uses statistical models to estimate the mutation proportion based on the frequency of the mutation in a smaller sample of individuals.

## 3. How do the A and B approaches differ?

The main difference between the A and B approaches is the method of data collection. The A approach relies on direct observation of the mutation in a larger sample size, while the B approach uses statistical models to estimate the mutation proportion based on a smaller sample size. Additionally, the A approach may be more accurate but requires a larger sample size, while the B approach is less accurate but can be used with smaller sample sizes.

The A approach has the advantage of providing a more accurate estimation of gene mutation proportion, but it requires a larger sample size and can be more time-consuming. The B approach, on the other hand, can be used with smaller sample sizes and is less time-consuming, but it may be less accurate. Additionally, the B approach relies on statistical models, which may be more complex and difficult to interpret for some researchers.

## 5. How can estimating gene mutation proportion benefit scientific research?

Estimating gene mutation proportion can provide valuable insights into the prevalence and potential risk factors of genetic disorders. This information can aid in the development of targeted treatments and prevention strategies. Additionally, understanding gene mutation proportion can also contribute to our understanding of genetic variation and evolution within a population.

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