Timeline of the history of scientific method Aristotle— BCE. A polymath, considered by some to be the father of modern scientific methodologydue to his emphasis on experimental data and reproducibility of its results. This is the greatest piece of Retroductive reasoning ever performed.
For example, "The toaster won't toast because the electrical outlet is broken" is a hypothesis, whereas "Electrical appliances need a source of electricity in order to run" is closer to a theory. A prediction is an outcome we'd expect to see if the hypothesis is correct. In this case, we might predict that if the electrical outlet is broken, then plugging the toaster into a different outlet should fix the problem.
If I plug the toaster into a different outlet, then it will toast the bread. To test the hypothesis, we need to make an observation or perform an experiment associated with the prediction.
For instance, in this case, we would plug the toaster into a different outlet and see if it toasts. Plug the toaster into a different outlet and try again. If the toaster does toast, then the hypothesis is supported—likely correct.
If the toaster doesn't toast, then the hypothesis is not supported—likely wrong. The results of a test may either support or contradict—oppose—a hypothesis.
Results that support a hypothesis can't conclusively prove that it's correct, but they do mean it's likely to be correct. On the other hand, if results contradict a hypothesis, that hypothesis is probably not correct. Unless there was a flaw in the test—a possibility we should always consider—a contradictory result means that we can discard the hypothesis and look for a new one.
And can we actually disprove a hypothesis? One key distinction here is between what's logically possible and what's practically possible. Logically speaking, it's impossible to prove a hypothesis, but possible to disprove one. Practically speaking, it's challenging to either prove or disprove a hypothesis beyond the slightest doubt.
Logical possibility As an example, suppose we have the hypothesis that all apples are red, and we test this hypothesis by examining a group of ten apples and seeing what color they are.
If all ten apples are red, our hypothesis is supported, but it's not proven: On the other hand, if one of our ten apples is green, we have—in a world of perfect information and no error—disproven our hypothesis. Practical possibility Practically speaking—"in real life"—it's still impossible to prove a hypothesis since it's not even logically possible to prove a hypothesis.
However, in real life scenarios, it also becomes difficult to disprove a hypothesis beyond any imaginable doubt. For example, suppose that we examine our apples in the scenario above and find that one of them is green.
If the green apple is bona fide, the hypothesis cannot be correct.
However, it's possible that the apple is not actually green, in the sense we care about, and that we classified it as a green apple due to an error or a wrong assumption. For example, perhaps the green apple is a decorative apple that someone painted. Or maybe it's a red apple covered with green mold, which makes it look green on first examination.
Building a body of evidence In a sense, we can never conclusively disprove the hypothesis that all apples are red in the real world because we can't exclude the tiny possibility of some kind of error, bad assumption, or bizarre coincidence.
However, suppose that we carefully investigate every alternative explanation we can think of—painted apples, moldy apples, etc. In addition, let's say we repeat our experiment by looking at a much larger number of apples, and we find a consistent fraction of green ones.
Additionally, people on neighboring farms report that they routinely see green apples as well. In this case, although our hypothesis that all apples are red may not be disproven beyond all imaginable doubt, it is so strongly contradicted as to be effectively disproven.
In other words, no one is likely to consider it correct, design experiments around it, or base assumptions on it. Many thanks to Andrew M. The last step of the scientific method is to reflect on our results and use them to guide our next steps.Approaches in Psychology Research  Nomothetic (Quantitative Approach)  This approach is basically used in inferential and descriptive statistics as both mediums of scientific method of investigation in analyzing, presenting, and interpretation of data gathered by the researcher through standardized or objective instruments (e.g.
psychological Tests). The Scientific Method Utilizing The Scientific Method SCI, Introduction to Science May 1, The Scientific method is a process that scientists use to solve a problem. It generally involves four distinct steps that constitute the “Scientific Method”.
This book shows how science works, fails to work, or pretends to work, by looking at examples from such diverse fields as physics, biomedicine, psychology, and economics.
It accepts the use of the scientific method, and generally rejects Introspection as a valid method of investigation, unlike phenomenological methods such as Freudian psychology. It posits the existence of internal mental states (such as beliefs, desires and motivations) unlike behaviourist psychology.
In this brief commentary, I propose an important reform: that psychological scientists should take scientific method seriously. By this, I mean that they should be knowledgeable about, and be guided in their research by, theories of scientific method. What is the scientific method and how is it used in psychology?
The scientific method is essentially a step-by-step process that researchers can follow to determine if there is some type of relationships between two or more variables.