Increase Accuracy of Program: How Many Subjects Needed?

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In summary, the conversation discussed using two variables, A and B, to determine if someone belongs to one group or the other. However, as more people are tested, it is found that some individuals do not fit neatly into either group and the model needs to be refined. The question of how many people need to be tested for an accuracy rating above 80% is asked, but the definition of "accuracy rating" is unclear and the question is too general. Simon suggests changing the model to account for a continuum between the groups and using a larger sample size to improve representativeness for the general population. After some work, a solution is found.
  • #1
jt.harperjr
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I'm a programmer, but I know very little about statistics and am not even sure where or how to ask this. Let's say you have 2 variables about people in general, var A and var B, that are tangible characterists of these people. People either possesses A or B.

I then take 11 different measurements about the person and use those to determine if they are actually A or B without looking at them. The program successfully determines if someone is A or B in a group of 10 people. But as I test more and more people, I find that some people have slight differences or exceptions in their variables that I have to account for.

Example: All A people have the first variable in a range of 12 to 13, the 2nd variable in a range of 5 to 6, but then I find an A person who has a range of 1 for the 2nd variable. So I add to the formula that if the 2nd variable = 1, then the person has A.

My question - How many people would I have to test out to get an accuracy rating above 80% of the program, or is that even possible. As I add more and more subjects that fit that equation, does that translate into an increase in accuracy of the program when used on the general population?
 
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  • #2
jt.harperjr said:
I'm a programmer, but I know very little about statistics and am not even sure where or how to ask this. Let's say you have 2 variables about people in general, var A and var B, that are tangible characterists of these people. People either possesses A or B.
So to start with your model assumes that someone is one or the other ... i.e. maybe A=gay and B=straight ... and you cannot be bisexual.

I then take 11 different measurements about the person and use those to determine if they are actually A or B without looking at them. The program successfully determines if someone is A or B in a group of 10 people. But as I test more and more people, I find that some people have slight differences or exceptions in their variables that I have to account for.

Example: All A people have the first variable in a range of 12 to 13, the 2nd variable in a range of 5 to 6, but then I find an A person who has a range of 1 for the 2nd variable. So I add to the formula that if the 2nd variable = 1, then the person has A.
So you discover with testing that it the initial model needs to be refined to account for those mostly straight people who have experimented with same-sex relationships in college or something?

Maybe you noticed that some people with mostly blue eyes have a bit of brown flecks in them or some people who are basically dark-skinned are, yet, not exactly black either.

My question - How many people would I have to test out to get an accuracy rating above 80% of the program, or is that even possible. As I add more and more subjects that fit that equation, does that translate into an increase in accuracy of the program when used on the general population?
You need to change your model. Perhaps you need to set A and B as opposite ends of a scale.
You also need to define what you mean by "accuracy rating".
But your question is too general.
 
  • #3
Simon Bridge said:
So to start with your model assumes that someone is one or the other ... i.e. maybe A=gay and B=straight ... and you cannot be bisexual.

So you discover with testing that it the initial model needs to be refined to account for those mostly straight people who have experimented with same-sex relationships in college or something?

Maybe you noticed that some people with mostly blue eyes have a bit of brown flecks in them or some people who are basically dark-skinned are, yet, not exactly black either.


You need to change your model. Perhaps you need to set A and B as opposite ends of a scale.
You also need to define what you mean by "accuracy rating".
But your question is too general.

To give more detail, in the example above, you could be bisexual. I was just trying to keep it simple. There is actually A, B, C, D, and E, they are on a scale going from A to E, placing people somwhere in between or on the ends.

By accuracy rating, I wonder if I incorporate enough subjects, would it translate to being more accurate for the worlds population as a whole?
 
  • #4
The bigger your sample, the more representative the results will be of the population responses - if you were to test the entire population. Does that mean it is "accurate"? Depends what you mean by "accurate". Depends how you choose your sample.

You also need a model that accounts for the continuum between the types.
Like height is pretty continuous, but someone who is 185cm tall is actually between 184.5cm and 185.4cm
You could be more granulated than that - using wider ranges for "small", "medium", "tall", "giant" etc.
 
  • #5
Thanks for your help Simon, after some work on the dry erase board, I've found an answer.
 
  • #6
Well done. I use a windowpane myself ;)
 

FAQ: Increase Accuracy of Program: How Many Subjects Needed?

1. How do you determine the sample size needed for the program?

To determine the appropriate sample size for a program, a power analysis can be conducted. This involves considering factors such as the desired level of significance, effect size, and power of the study.

2. Is there a specific formula for calculating the number of subjects needed?

While there are various formulas that can be used for calculating sample size, the most commonly used is the sample size formula for comparing two independent means. This formula takes into account the factors mentioned above and provides an estimate for the number of subjects needed.

3. How can increasing the sample size improve the accuracy of the program?

Increasing the sample size can improve the accuracy of a program by reducing the margin of error and increasing the power of the study. This means that the results will be more representative of the population and can be generalized with more confidence.

4. Are there any other factors that can affect the accuracy of the program, aside from sample size?

Yes, there are other factors that can affect the accuracy of a program. These include the design of the study, the quality of data collection, and the control of confounding variables. It is important to consider and address these factors in order to increase the accuracy of the program.

5. How can we ensure that the chosen sample size is sufficient for the program?

Conducting a power analysis and choosing a sample size based on the results can help ensure that the chosen sample size is sufficient for the program. Additionally, conducting a pilot study or consulting with other experts in the field can also help determine if the sample size is appropriate.

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