Steps to Identify a Failed Event

Step 1. Are you doing something… anything? One cannot fail unless one actual tries to do something first. This step may seem overly presumptive, but many people avoid doing anything based on a theory that if they do nothing, then they cannot do anything wrong, hence they will not fail. The first step is to do something… anything.

Step 2. Observe everything. Use your senses to observe objects and events while searching for patterns. To observe, we must use all of our senses – sight, smell, touch, taste and hearing. Our body is an amazing data gathering device. It can be highly qualitative; it can even estimate quantities at times. For instance in tasting wines, aficionados speak of a licorice/peppery nose, a peach and plum palate with an oaken, tannin finish. However not everyone can sense all of these flavors at first. Sensitivity can be learned with practice and guidance, allowing the body to absorb all of the data, swishing the wine around to different parts of the tongue, throat and nose. Very sensitive individuals can smell the slightest scent of a cologne or aromatic food source such as cheese or herbs. Some people notice animals who are camouflaged; others can sense the magnetic resonance of someone nearby. We all observe and have our specialized abilities to observe more keenly. Observing leads to curiosity, questions, and interpretations about the meaning of the data.

Step 3. Collect data. There is a little scientist in all of us. We gather many things in life. Some of us gather nick nacks, others shells, and still others dust bunnies. Regardless, we all seem to gather something and those who do not have usually decided not to gather. Not gathering is in itself a negative gathering and may be as important an attribute as gathering in this case. So, as we gather stuff, we can also gather information or data. The data can include what many of us think when we hear the word data – empirical, quantitative, numerical – or it can be more qualitative and descriptive in nature.

Step 4. Make sense of the data. To do this well, we need to measure and communicate. We do this by using graphs, charts, maps, symbols, diagrams, and other visual representations. They must be clear, precise, and unambiguous if they are to be effective and interpreted as intended. Graphical representations are used frequently because they can present a large amount of data in a small area and allow the user to make comparisons between important data points. The comparisons and patterns can help us to make inferences about what the data means to inform subsequent events.

Step 5. Infer. Inference is an interpretation of an observation. Here we recognize patterns and expect them to reoccur under the same conditions. Ultimately we form hypotheses based on inferences. Inferences typically rely heavily on our past experiences and what we can observe. For instance, if I look out the window and see a flag waving, I can infer that it is windy outside. This phenomenon occurs in the present and relies on the prior knowledge that I have seen a flag wave outside before and I noted that it was windy. Inferences occur in the present and are interpolated data – data between two known points. They are useful, but do not tell the whole story.

Step 6. Predict. Forecast a possible future observation. This ability to construct unknown scenarios allows us to preplan appropriate behavior based on reason, careful observation, and inferences made about relationships among the observations. “If this happens, what will follow?” “What will happen if I do this? For instance, if I dropped a zucchini and it fell to the floor and hit my shoe, I might think that was an accident. If I dropped the same zucchini, the same way on the same afternoon in the same place with the same atmospheric conditions and the same music playing in the background, wearing the same shoes, ten times, then I might be able to infer if I dropped the zucchini again, it would fall to the floor and hit my shoe. Predictions are good, because they allow us to tell the future before it occurs. The drawback of predictions, of course, is that they are not always correct. The meteorologist has expert training in the use of observations and data, and her predictions are probably much better than our own, but there are always unforeseen factors that influence events. Predictions are useful. However they are extrapolated beyond two data points, so they have their limits.