Say one has an assumption and forms a theory that makes good predictions using that assumption. Now say that later, someone else comes up with a different assumption and using that new assumption (while abandoning the old assumption), is able to make more good predictions.
In doing this type of study, how big should the sample be to have confidence in the new theory? What else would you do to try to establish the new theory? How would you try to disprove the old assumption?
PhilX
Science assumptions and theorizing

 Posts: 5621
 Joined: Sun Aug 31, 2014 7:39 am

 Posts: 4029
 Joined: Fri Oct 25, 2013 6:09 am
Re: Science assumptions and theorizing
If a hypothesis is testable that shall determine whether or not it is valid but untestable
hypotheses are non scientific regardless of how logical or rational they may actually be
hypotheses are non scientific regardless of how logical or rational they may actually be

 Posts: 5621
 Joined: Sun Aug 31, 2014 7:39 am
Re: Science assumptions and theorizing
Do you have anything to add about sampling?surreptitious57 wrote:If a hypothesis is testable that shall determine whether or not it is valid but untestable
hypotheses are non scientific regardless of how logical or rational they may actually be
PhilX

 Posts: 4029
 Joined: Fri Oct 25, 2013 6:09 am
Re: Science assumptions and theorizing
Sampling should be large and varied as possiblePhilosophy Explorer wrote:
Do you have anything to add about sampling

 Posts: 5621
 Joined: Sun Aug 31, 2014 7:39 am
Re: Science assumptions and theorizing
The following I've copied from Stattrek:
"As noted earlier, finding the "best" sampling method is a fourstep process. We work through each step below.
List goals. This study has two main goals: (1) maximize precision and (2) stay within budget.
Identify potential sampling methods. This tutorial has covered three basic sampling methods  simple random sampling, stratified sampling, and cluster sampling. In addition, we've described some variations on the basic methods (e.g., proportionate vs. disproportionate stratification, onestage vs. twostage cluster sampling, sampling with replacement versus sampling without replacement).
Because one of the main goals is to maximize precision, we can eliminate some of these alternatives. Sampling without replacement always provides equal or better precision than sampling with replacement, so we will focus only on sampling without replacement. Also, as long as the same clusters are sampled, onestage cluster sampling always provides equal or better precision than twostage cluster sampling, so we will focus only on onestage cluster sampling. (Note: For cluster sampling in this example, the cost is the same whether we sample all students or only some students from a particular cluster; so in this example, twostage sampling offers no cost advantage over onestage sampling.)
This leaves us with four potential sampling methods  simple random sampling, proportionate stratified sampling, disproportionate stratified sampling, and onestage cluster sampling. Each of these uses sampling without replacement. Because of the need to maximize precision, we will use Neyman allocation with our disproportionate stratified sample.
Test methods. A key part of the analysis is to test the ability of each potential sampling method to satisfy the research goals. Specifically, we will want to know the level of precision and the cost associated with each potential method. For our test, we use the standard error to measure precision. The smaller the standard error, the greater the precision...."
I know that time is a factor too.
I've done sampling work before and I've established the the advice offered does work, but the reasons given were wrong. I replaced the assumptions with new ones which allowed me to successfully generalize and extend the offered advice (with reinterpretation ofc).
The work I've done is uncompleted.
PhilX
"As noted earlier, finding the "best" sampling method is a fourstep process. We work through each step below.
List goals. This study has two main goals: (1) maximize precision and (2) stay within budget.
Identify potential sampling methods. This tutorial has covered three basic sampling methods  simple random sampling, stratified sampling, and cluster sampling. In addition, we've described some variations on the basic methods (e.g., proportionate vs. disproportionate stratification, onestage vs. twostage cluster sampling, sampling with replacement versus sampling without replacement).
Because one of the main goals is to maximize precision, we can eliminate some of these alternatives. Sampling without replacement always provides equal or better precision than sampling with replacement, so we will focus only on sampling without replacement. Also, as long as the same clusters are sampled, onestage cluster sampling always provides equal or better precision than twostage cluster sampling, so we will focus only on onestage cluster sampling. (Note: For cluster sampling in this example, the cost is the same whether we sample all students or only some students from a particular cluster; so in this example, twostage sampling offers no cost advantage over onestage sampling.)
This leaves us with four potential sampling methods  simple random sampling, proportionate stratified sampling, disproportionate stratified sampling, and onestage cluster sampling. Each of these uses sampling without replacement. Because of the need to maximize precision, we will use Neyman allocation with our disproportionate stratified sample.
Test methods. A key part of the analysis is to test the ability of each potential sampling method to satisfy the research goals. Specifically, we will want to know the level of precision and the cost associated with each potential method. For our test, we use the standard error to measure precision. The smaller the standard error, the greater the precision...."
I know that time is a factor too.
I've done sampling work before and I've established the the advice offered does work, but the reasons given were wrong. I replaced the assumptions with new ones which allowed me to successfully generalize and extend the offered advice (with reinterpretation ofc).
The work I've done is uncompleted.
PhilX
Who is online
Users browsing this forum: No registered users and 4 guests