The scientific method is a standardized way of making observations, gathering data, forming theories, testing predictions, and interpreting results. Does this mean all scientists follow this exact process? No. Some areas of science can be more easily tested than others.
For example, scientists studying how stars change as they age or how dinosaurs digested their food cannot fast-forward a star's life by a million years or run medical exams on feeding dinosaurs to test their hypotheses. When direct experimentation is not possible, scientists modify the scientific method. In fact, there are probably as many versions of the scientific method as there are scientists!
But even when modified, the goal remains the same: to discover cause and effect relationships by asking questions, carefully gathering and examining the evidence, and seeing if all the available information can be combined in to a logical answer.
1. Research must be Replicable, meaning that other researchers must be able to repeat the study and get the same results. This is why in a scientific study, researchers take the time not only to describe their results but also the methods they used to achieve their results.
As scientists do their research and make sure that it's replicable, they'll develop a theory and translate that theory into a hypothesis. A Hypothesis is a testable prediction of what will happen given a certain set of conditions. A good theory must do two things: organize many observations in a logical way and allow researchers to come up with clear predictions to check the theory.
A good theory or hypothesis also must be Falsifiable, which means that it must be stated in a way that makes it possible to reject it. In other words, we have to be able to prove a theory or hypothesis wrong.
Theories and hypotheses need to be falsifiable because otherwise research will present confirmation bias. Researchers who display Confirmation Bias look for and accept evidence that supports what they want to believe and ignore or reject evidence that refutes their beliefs.
Falsifiability doesn’t mean that there are currently arguments against a theory, only that it is possible to imagine some kind of argument which would invalidate it. Falsifiability says nothing about an argument's inherent validity or correctness. It is only the basic requirement of a theory which allows it to be considered scientific. An important note however, is that falsifiability is not simply any claim that has yet to be proven true.
By stating hypotheses precisely, scientists ensure that they can replicate their own and others’ research. To make hypotheses more precise, researchers use operational definitions to define the variables they study. Operational Definitions state exactly how a variable will be measured.
Precision and accuracy are two ways that scientists think about error. Accuracy refers to how close a measurement is to the true or accepted value. Precision refers to how close measurements of the same item are to each other. Precision is independent of accuracy which means it is possible to be very precise but not very accurate, and it is also possible to be accurate without being precise. The best quality scientific observations are both accurate and precise.
The easiest way to illustrate the difference between precision and accuracy is with the analogy of a dartboard.
Parsimonious means “being thrifty or stingy.” A person who values parsimony will apply the thriftiest or most logically economical explanation for a set of phenomena.
The Principle Of Parsimony, also called Occam’s Razor, maintains that researchers should apply the simplest explanation possible to any set of observations. For instance, scientists try to explain results by using well-accepted theories instead of elaborate new hypotheses. Parsimony prevents researchers from inventing and pursuing outlandish theories.
What Parsimony means in practice is we should go with the weight of the evidence available to us. This will probably seem very obvious, but in practice it is essential that we have a philosophically justified method of choosing between explanations of our data. After all, when there is good evidence to support one idea and only slightly less good evidence to support another – can you really chose between them? Well, yes. You *MUST* take number 1.
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