Deductive reasoning is the opposite process to inductive reasoning. In general, terms, inductive reasoning takes a specific example, or examples, and induces that they can be applied to a much larger group.
Deductive reasoning, by contrast, starts with a general principle and deduces that it applies to a specific case. Inductive reasoning is used to try to discover a new piece of information; deductive reasoning is used to try to prove it.
Every day, I get in my car to leave for work, at eight o'clock. Every day, the journey takes 45 minutes, and I arrive at work on time. If I leave for work at eight o'clock today, I will be on time.
Today, I left for work at eight o'clock, and was on time. Therefore, every day that I leave the house at eight o'clock, I will arrive at work on time.
The deductive statement is a perfectly logical statement, but does rely upon the initial premise being correct. Perhaps today, there are roadworks, so you will end up being late for work. This is why any hypothesis can never be completely proved, because there is always the scope for the initial premise to be wrong.
Inductive reasoning, whilst commonly used in science, is not logically valid, because it is not strictly accurate to assume that a general principle is correct. In the above example, perhaps 'today' is a weekend, with less traffic. It is illogical to assume an entire premise, just because one specific data set seems to suggest it.
This is not to say that inductive reasoning has no place in scientific processes, because it is an extremely useful tool. Even mathematicians use the process, to look at a specific phenomenon and assess the possibility that it is true in all cases. Deductive reasoning is then used to construct a logical and rigorous proof.
There is, however, one major weakness in deductive reasoning, a trap into which a scientist should not fall. Deductive reasoning relies heavily upon the initial premise being correct. If this premise is incorrect, not only does it jeopardize the deductive reasoning, but the whole process of logic. Certain philosophers have argued that deductive reasoning is an unattainable ideal, and that all scientific deduction is defeasible.
Many branches of applied science work around this, by assigning probabilities to events and outcomes. Whilst not a strict application of the scientific method, it is useful where incorrect deductions could be devastating.
For example, weather forecasting is an area where deductive reasoning probabilities are often used. A meteorologist will look at the data, and using their skill and judgment, decide upon the likely weather for that day. They are aware that a certain pattern of initial conditions frequently leads to a certain weather type. However, they will never say that it is definitely going to rain, because the weather is too unpredictable, and they can never be sure that their initial assumptions are correct.
Michael Fish, The respected British meteorologist, in 1987, stated, categorically, that there was no chance of a hurricane hitting Southern England. He was wrong, and the unprepared country was devastated. The initial premises of his deductive reasoning were wrong, so now forecasters always warn of adverse weather as a percentage chance, affording people the choice of preparing for the worst.
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