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Falsification in our example
Consider first only the two models that we have
treated so far. If we can show that one of the models is incorrect, we will
more readily accept the other as being probable, but if we can prove that
both are incorrect then we would have to admit that we could not explain
why the mussels were distributed as observed. If this case should arise,
we would have to find a new model that could explain what we have observed.
It is easier to claim with certainty that a model is incorrect
, than correct. If from a model we can predict certain things and they are
fulfilled in a new situation, then the model can be correct, but there can
be other explanations that predict the same occurrence. One of them can
be right. Whereas if a model predicts certain occurrences that are not fulfilled,
then the model must be incorrect, it could not explain what happened. The
purpose of the scientific procedure that we have described here was to demonstrate
an incorrect model. One knows if a model is incorrect if its predictions
are not fulfilled. This method is known as falsification.
A simple example of falsification
A simple example that elucidates the principal
of falsification:
”Along the same beach, red algae always grows deeper
than green algae". It is difficult to know if this statement is true. The only thing we know for sure is that red algae always grows deeper
than green algae when all beaches have been controlled, but to control all
beaches is an impossible task. This difficulty does not exist when we have
knowledge of that the assertion is incorrect. If we find one or several beaches
where at least one red alga grows more shallow than a green alga, we can conclude
that the statement that all red algae grow deeper than green algae is
incorrect.
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