Which stats test to use in this situation

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In summary: Instead, you would need to gather data on the ages of people who were born in your town in 1930 and then died at any point in time. This would provide a more accurate comparison to the national average life expectancy for males born in 1930. In summary, the person is unsure of which statistical tests to use for their two-fold research on the average life expectancy in their town compared to the national average and the impact of vaccines on life expectancy. They plan on grouping ages into 5-year intervals and will need recent death data and the assumption that burial at the cemetery is independent of age at death. However, it may be difficult to compare the average life expectancy for males born in their town in 1930 to the national
  • #1
TrpnBils
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This is a two-fold statistical thing I want to pursue and I'm unsure of which test(s) to use. Using data of gender and age at time of death from a large, old cemetery in town I want to compare two things:

1. Average life expectancy of citizens of our town compared to the national average
2. Whether there is a noticeable difference in life expectancy after the development of major vaccines over the past 100 years.

I'm figuring I will lump ages into groups of 5 years (died at ages 0-5, 6-10, etc) so there will be multiple groups, not including separation of genders.

Thoughts?
 
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  • #2
To get the current life expectancy, I think you will need data about recent deaths, together with the age distribution* in your town.
You also need the assumption (or data) that "gets buried at the cemetery" is independent of the age at time of death.

2. Whether there is a noticeable difference in life expectancy after the development of major vaccines over the past 100 years.
Same issue, and there it is even harder to get the age distribution in the living population. It might be a reasonable assumption that moves in/out of town are independent of the age*, in that case you can just count how frequent different ages are.* a simple example why this is relevant: imagine a small town with a big old people's home. Many old people will move to that town and die there. Does this mean that all inhabitants there reach that age? Of course not.
 
  • #3
So is it even possible then to compare something like the average life expectancy of a male born in my town in 1930 to the national average for males in that year?

It doesn't seem like that should be that difficult
 
  • #4
It is possible, but it is not easy. It is not sufficient to count gravestones for deaths in 1930 (or any other year).
 
  • #5


For the first question, comparing the average life expectancy of citizens in your town to the national average, you could use a two-sample t-test or a one-way ANOVA. This will allow you to determine if there is a significant difference between the two groups.

For the second question, whether there is a noticeable difference in life expectancy after the development of major vaccines, you could use a two-sample t-test or a one-way ANOVA as well. However, since you are planning to group the ages into 5-year intervals, you may also want to consider using a Chi-square test to compare the proportions of individuals who lived past a certain age (e.g. 80 years old) before and after the development of vaccines.

Overall, it is important to consider the assumptions and limitations of each statistical test and choose the most appropriate one based on your specific research question and data. It may also be helpful to consult with a statistician or conduct a power analysis to determine the appropriate sample size for your study.
 

Related to Which stats test to use in this situation

1. What is the purpose of using a statistical test?

The purpose of using a statistical test is to determine whether there is a significant difference or relationship between variables in a given dataset. It helps to make objective and evidence-based conclusions about a population based on a sample of data.

2. How do I know which statistical test to use in a specific situation?

The choice of statistical test depends on the type of data you have and the research question you want to answer. You should consider the type of variables (categorical or numerical), the number of groups or variables being compared, and the assumptions of the test. Consulting with a statistician or using online resources can also help in determining the appropriate test.

3. What is the difference between a parametric and non-parametric test?

A parametric test assumes that the data follows a specific distribution, such as a normal distribution, and is used when certain statistical assumptions are met. A non-parametric test does not make any assumptions about the underlying distribution of the data and is often used when the data does not meet the assumptions of a parametric test.

4. Can I use multiple statistical tests on the same dataset?

It is generally not recommended to use multiple tests on the same dataset, as this can increase the chances of obtaining a false positive result. Instead, it is better to choose one appropriate test and interpret the results carefully.

5. What should I do if my data does not meet the assumptions of the chosen statistical test?

If your data does not meet the assumptions of the chosen test, you may need to use a different test or try transforming the data to meet the assumptions. It is important to carefully consider the limitations and potential biases of using a statistical test on data that does not meet its assumptions.

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