- Thread starter oscarwukaka
- Start date

If you are wanting to use your data to infer something about a larger group, then, (ignoring the all the problems with convenience sampling as opposed to random sampling), the whole idea of a power/sample size analysis is to avoid a "not significant" conclusion. "Not significant" does not mean there is no difference. It is really just a face saving way of saying "After all the work I've done and all resources I've used, I still don't know if there is a difference or not" which is not a good admission in a study report.

If you are sure that your study will be valid with convenience sampling, and you want to make inferences about populations, and you want to be reasonably sure that your work and resources aren't wasted, then you should do a power/sample size analysis to see if your study is feasible.

If you are wanting to use your data to infer something about a larger group, then, (ignoring the all the problems with convenience sampling as opposed to random sampling), the whole idea of a power/sample size analysis is to avoid a "not significant" conclusion. "Not significant" does not mean there is no difference. It is really just a face saving way of saying "After all the work I've done and all resources I've used, I still don't know if there is a difference or not" which is not a good admission in a study report.

If you are sure that your study will be valid with convenience sampling, and you want to make inferences about populations, and you want to be reasonably sure that your work and resources aren't wasted, then you should do a power/sample size analysis to see if your study is feasible.

However, it may possibly depend on how you plan your study. Many (most) studies have some form of convenience sampling because it is usually impractical to ensure that every member of your population has an equal chance of being selected and you are limited to what is available. A good design can overcome this problem with randomization, controls, replication, and other statistical techniques.

However, it may possibly depend on how you plan your study. Many (most) studies have some form of convenience sampling because it is usually impractical to ensure that every member of your population has an equal chance of being selected and you are limited to what is available. A good design can overcome this problem with randomization, controls, replication, and other statistical techniques.

However, it may possibly depend on how you plan your study. Many (most) studies have some form of convenience sampling because it is usually impractical to ensure that every member of your population has an equal chance of being selected and you are limited to what is available. A good design can overcome this problem with randomization, controls, replication, and other statistical techniques.