will explain when accepted.
Principle 1: Ruling out alternative explanations – Usually the results of any single study are consistent with several different explanations (or hypotheses) and additional research is often needed to decide which explanation/hypothesis is best supported. When looking at a pattern of results that has been reported from a study, it is important to ask “are there any alternative hypotheses that could explain this pattern of data?” That is, we should consider whether there are any other reasons why the researchers might have found the particular results that they found in their study. Maybe there was a confounding variable in an experiment that could offer a different explanation for the results, other than the one that the researchers have given. The alternative explanations that are most important to acknowledge are those that could explain the specific pattern of results that has been found in the study. It is useful to consider how we could attempt to rule out these alternative hypotheses.
Principle 2: Correlation vs. causation – A correlation between two things (a statistical association) does not necessarily mean there is a cause-and-effect relationship between them. If a pattern of results was produced simply by measuring two different things and comparing them, we cannot say anything for sure about whether one of these things caused the other; all we can say is that the two things go together. When a causal claim (e.g., A causes B) is made from a correlation, it’s always important to ask whether the causal connection could be reversed (i.e., B causes A) or whether a third variable could explain the relationship (i.e., A and B do not cause each other; instead C causes A and B to go together). If there is more than one possible pattern of cause-and-effect that could result in a correlation, we cannot use that correlation as evidence that any one specific pattern is necessarily true.
Principle 3: Falsifiability – Scientific claims must be
capable of being disproved. In other words, we should be able to think of a way to test whether or not a claim is true; there should be data we can collect that tell us if our hypothesis is likely to be true or false. If the claim is made in such a way that there’s no good way to test it, the claim is not really scientific. In science, we should always be open to the possibility that our ideas are wrong. If there are no data that could convince us that our ideas are wrong, then our ideas are not properly scientific. The idea behind this principle is that, for ideas to be scientific, there ought to be a way to test those ideas; there should be a way to show either that the idea might be correct, or that it might be false. So, to be considered scientific, a researcher has to allow his or her ideas to be tested and to be open to the possibility that studies might show his or her ideas were wrong.
Principle 4: Replicability – Scientific findings must be capable of being duplicated following the same methodology. In other words, in science, other people must be able to follow our methods and should get similar results. In addition, the most reliable claims are those that have converging evidence for them. We can only really be confident in a claim if it has been tested in multiple different ways and all of them point to the same effect. Before we can be confident in scientific claims, it is important that the studies they are based on can be, and are, replicated. In other words, a properly scientific claim is one built on data from studies that can be done many times, either in exactly the same way or in a slightly different way, and which when done, all show similar results. This is because there is nearly always the possibility that the results of a single study were flawed in some way, or maybe even just the result of chance.
Principle 5: Extraordinary claims – Science is, for the most part, a cumulative process, where new claims represent small advances over older ones. A claim that contradicts what we already know, or that seems to promise to completely explain or solve a complex problem in a new way, must have a lot of evidence to back it up. The bigger the claim, the more evidence must be provided.
Principle 6: Parsimony (a.k.a. Occam’s razor) – If two hypotheses explain a phenomenon equally well, in science we generally prefer the simpler explanation. The simpler explanation is not necessarily correct, but we should start by using that explanation and only make a more complicated one when the simple explanation cannot account for our results. In other words, we shouldn’t make our explanations more complicated than necessary.
