First, formulate your scientific research question starting with Why or How. Then apply Strong Inference following Platt’s basic three-step cycle: 1. Devise alternative hypotheses; 2. Devise a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses; 3. Carrying out the experiment so as to get a clean result; Recycle the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain; and so on. In more detail: 1. Observed and inferred facts inspire a question. 2. The question inspires one or, preferably, more hypotheses. This is a creative process. Several hypotheses may be proposed, and they need not have a high likelihood of being supported, but a good hypothesis must be an explanatory statement that is testable. 3. The hypotheses are deliberately subjected to falsification by, first, stating the logical consequences of the hypotheses. Statements in the form "if (the hypothesis), then (the consequences)" are useful. 4. Next, the accuracy of the predicted consequences are tested by the acquisition of new facts from experimentation, or observation, or from the body of known facts not already used to formulate the hypotheses. 5. Incompatibility between prediction and outcome leads to the rejection of hypotheses, and compatibility leads to tentative acceptance. In all cases, repeated incompatibility or compatibility from separate lines of testing is desirable. 6. The hypotheses, together with the facts and the record of the inferential process, are submitted to public scrutiny and may become accepted as public knowledge.  _Observe_. All science starts with observation, but only those observations that are puzzling to us have any value, because they are the ones that signal to us that our current explanation might not be correct. Skilled observation requires sensitivity to our environment and an ability to recognize when something is confusing and therefore worth pursuing. _Question_. Puzzling observations organically lead us to ask non-trivial questions, which are generally ones that begin with ‘why’ or ‘how’. Many students harbor the misconception that scientific questions must take the form, ‘What is the effect of _x_ (independent variable) on _y_ (dependent variable)?’ In fact, answering these kinds of questions typically does not require the scientific method, because it is immediately obvious how to answer them (vary _x_, measure _y_). In contrast, it is rarely evident at first how to go about answering ‘how’ and ‘why’ questions and this is where strong inference can provide us with a roadmap. _Hypothesize_. Platt suggests that we enter difficult problems by devising alternative hypotheses, and my students are shocked to learn that coming up with hypotheses requires nothing less than creativity. When students ask me where good hypotheses come from, I tell them, ‘the same place good poetry comes from’, which is of course a maddening way of saying ‘I don't know.’ Because so many students possess the misconception that a hypothesis is simply an ‘educated guess’ about how an experiment will turn out (i.e. a prediction), I find it useful to apply a simple test to check whether a hypothesis is a good one. The test of a good hypothesis is to ask whether it represents a satisfying answer to the question that has been posed. If it does not, then it is not worth pursuing. A hypothesis may pass this acid test, but get discarded later because we realize it violates a law of physics or is internally inconsistent. With several cycles of creativity followed by criticism, we can whittle down our list of hypotheses to a handful of reasonable explanations. If we assume that one hypothesis is correct and the others are wrong, this is where the real fun begins, because it is at this stage that the whole enterprise starts to feel like a detective story. _Predict_. Platt tells us next to devise a crucial test, and students are always eager to do this, but I find it useful to insert one more step before the experiments are designed, and that is to list the predictions that each hypothesis makes. I constantly need to remind students that these are not predictions that you make, these are predictions that each hypothesis makes (Hutto, 2012). When we predict instead of letting the hypothesis predict, we lose the tight connection between hypothesis and experiment, and the logical structure of the entire process can fall apart. Finding predictions requires large doses of imagination, because we must try a hypothesis on for size and conjure up how the world would look if it were true. Once we have a list of predictions from each hypothesis, it is important to confirm that they are critical predictions. We can evaluate a prediction's utility by asking ourselves whether the hypothesis can survive if the prediction is found to be false. If it can, then it is not a strong prediction, and probably not worth testing. Focusing on tests with the greatest potential to disprove our hypotheses is important, because it is the fastest way to eliminate faulty explanations that might otherwise stand in our way of reaching the truth. _Test_. The testing step, sometimes called the experiment step, is when we evaluate whether a prediction is true by comparing it with some aspect of the real world. Much has been written about the ins and outs of experimental design, because there are lots of places where one can go wrong. Platt deliberately says little about this in his paper, because his intention was to illuminate those steps of the scientific method that he felt were being ignored. The test of a good experiment or test is to ask whether the results, whichever way they turn out, will allow you to evaluate how good a given prediction is. _Analyze and conclude_. The last step is to analyze and conclude, and if all the other steps have been carried out properly, this should be easy, and we should find ourselves closer to an answer to our question. If we have neglected certain parts, the logical bones of our structure might not be sound, and we are at risk of making an erroneous conclusion. Of course the process is not a linear one, and data collected during the testing stage may (and very often do!) become new puzzling observations of their own, which can lead to interesting questions and entirely new lines of inquiry. ## Example: Salmon Homing Experiment - **Central question**: How do salmon find their birth stream? - **Hypothesis branch 1**: Salmon use sight. - **Experiment**: Shield salmon’s eyes. - **Result**: Salmon still find stream → Refute “sight” hypothesis. - **Hypothesis branch 2**: Salmon use smell. - **Experiment**: Block sense of smell. - **Result**: Salmon fail to find stream → Support “smell” hypothesis and prune others. Source: [Strong Inference: The Way of Science](https://oono-lab.eemb.ucsb.edu/sites/default/files/2022-01/strong-inference-the-way-of-science.pdf) Source: [Fifty years of J. R. Platt's strong inference](https://journals.biologists.com/jeb/article/217/8/1202/13095/Fifty-years-of-J-R-Platt-s-strong-inference) by Douglas S. Fudge, _J Exp Biol_ (2014) 217 (8): 1202–1204. Reference: [Strong Inference](https://courses.cs.duke.edu/fall04/cps296.2/science_platt.html) by John R. Platt, _Science_ (1964), Volume 146, Number 3642