Science assumptions and theorizing

How does science work? And what's all this about quantum mechanics?

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Philosophy Explorer
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Science assumptions and theorizing

Post by Philosophy Explorer » Fri Mar 18, 2016 5:59 am

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

surreptitious57
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Re: Science assumptions and theorizing

Post by surreptitious57 » Fri Mar 18, 2016 6:40 am

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

Philosophy Explorer
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Re: Science assumptions and theorizing

Post by Philosophy Explorer » Fri Mar 18, 2016 6:45 am

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
Do you have anything to add about sampling?

PhilX

surreptitious57
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Re: Science assumptions and theorizing

Post by surreptitious57 » Fri Mar 18, 2016 6:57 am

Philosophy Explorer wrote:
Do you have anything to add about sampling
Sampling should be large and varied as possible

Philosophy Explorer
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Re: Science assumptions and theorizing

Post by Philosophy Explorer » Fri Mar 18, 2016 7:16 am

The following I've copied from Stattrek:

"As noted earlier, finding the "best" sampling method is a four-step 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, one-stage vs. two-stage 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, one-stage cluster sampling always provides equal or better precision than two-stage cluster sampling, so we will focus only on one-stage 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, two-stage sampling offers no cost advantage over one-stage sampling.)

This leaves us with four potential sampling methods - simple random sampling, proportionate stratified sampling, disproportionate stratified sampling, and one-stage 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

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