Answer:
A hypothesis can be tested and accurately evaluated using different types of data collection, such as experiments that aim to produce a quantifiable or observable reaction, trials that replicate experiments to test their effectiveness (such as medication trials), or other methods of data collection.
Step-by-step explanation:
There are no predetermined standards for observational evidence, often known as "empirical evidence," in matter science, other than the fact that it is helpful. The standards have evolved over time, just as science itself has. In fact, science only began to be effective with the advent of observation and test by observation.
Utilizing the century-old but still relevant notion of observation as a statistical hypothesis test, like any other hypothesis test, against observed parameters is one method to arrange observational data. A basic model like this may be used to deduce many things, but for our needs we can extract the following criteria (Iredia has previously addressed some of them):
- Observable
- Quantifiable
- Testable
- Useful uncertainty
- Repeatable
For each science and type of test, a different quality criterion is used to evaluate the uncertainty sigma factor. But broadly speaking, consider this comparison:
- Astronomy often use 7 sigma for observations, since they look at lots of systems and hence need to be vary of “once off” circumstances.
- Particle physics often use 5 sigma for observations, since they look at lots of energies and hence need to be vary of “look elsewhere” effects.
- Physics often use 3 sigma for observations, since they look at simple systems.
- Biologists often use 2 sigma for observations, since they look at complicated systems.
- Medicine may often have 80 % correct diagnosis and 60 % efficiency of treatments, which translates to 1–2 sigma uncertainty in outcome.
It is useful as long as we maintain above 50% uncertainty with some degree of repeatability.
Thank you,
Eddie