When You Form General Ideas and Rules Based on
Inductive reasoning can often be hidden in a deductive argument. That is, a generalization obtained by inductive reasoning can be reversed and used as the initial truth for a deductive argument. For example, the empirical school of ancient Greek medicine used epilogism as a method of inference. «Epilogism» is a method without theory that looks at history through the accumulation of facts without major generalizations and taking into account the consequences of causal claims.  Epilogism is a conclusion that moves entirely into the realm of visible and obvious things, it tries not to invoke the unobservable. Inductive reasoning is a method of reasoning in which premises are considered proof, but not complete certainty, of the truth of the conclusion.  It is also described as a method in which one`s own experiences and observations, including what is learned from others, are synthesized to obtain a general truth.  Many dictionaries define inductive reasoning as the derivation of general principles from specific observations (arguing from specific to general), although there are many inductive arguments that do not have this form.  For example, Albert Einstein observed the movement of a pocket compass when he was five years old and was fascinated by the idea that something invisible in the space around the compass needle moves it. This observation, combined with additional observations (e.g. of moving trains) and the results of logical and mathematical tools (deduction), resulted in a rule that matched his observations and could predict events that had not yet been observed. Deductive reasoning: guaranteed conclusionDeductive reasoning begins with the application of a general rule and from there moves to a specific conclusion guaranteed. Deductive reasoning moves from the general rule to the specific application: in deductive reasoning, the conclusion must also be true if the original statements are true.
For example, mathematics is deductive: inductive reasoning is also called hypothesis construction because all conclusions are based on current knowledge and predictions. [ref. needed] As with deductive arguments, biases can distort the correct application of inductive arguments, preventing the argumentator from drawing the most logical conclusion based on the evidence. Examples of these biases include availability heuristics, confirmation bias, and predictable global bias. But what is the difference between inductive and deductive? Overall, the difference is whether the reasoning shifts from general to specific or from specific to general. In this article, we`ll define each word with simple words, provide several examples, and even ask yourself if you can tell the difference. The predictable global bias revolves around the tendency to perceive order where it has not been proven, neither at all nor at some level of abstraction. Gambling, for example, is one of the most popular examples of predictable distortions in the world. Players often begin to think that they see simple and obvious patterns in the results and therefore believe that they are able to predict the results based on what they have seen.
In reality, however, the outcomes of these games are difficult to predict and very complex. In general, people tend to look for some sort of simplified order to explain or justify their beliefs and experiences, and it is often difficult for them to realize that their perception of order may be completely different from the truth.  Positivism, developed by Saint-Simon and promulgated in the 1830s by his former student Comte, was the first philosophy of science of the late modern era. After the French Revolution, Comte rejected metaphysics for fear of the ruin of society. Human knowledge evolved from religion to metaphysics and science, said Comte, who moved from mathematics to astronomy, physics, chemistry, biology to sociology — in that order — describing increasingly complicated fields. All the knowledge of the Society had become scientific, the questions of theology and metaphysics were unanswered. Comte found enumerative induction reliable because it was based on available experience. He claimed that science, instead of metaphysical truth, was the correct method for improving human society. This is enumerative induction in its weak form. It reduces «everything» to a single instance and greatly increases the likelihood of its conclusion through a much weaker assertion. Otherwise, it has the same flaws as the strong form: its sample population is not random and the quantification methods are elusive.
Here is an example of deductive reasoning: chickens are birds; all birds lay eggs; Therefore, chickens lay eggs. Another way to think about it: if something applies to a general class (birds), it also applies to class members (chickens). Inductive is used to describe the reasoning in which certain observations, such as observed patterns, can be used to draw a general conclusion. This method is sometimes called induction. Induction begins with a set of premises based primarily on experience or experimental evidence. He uses these premises to generalize a conclusion. An inductive prediction draws conclusions about a future instance from a past sample. Like an inductive generalization, an inductive prediction usually relies on a data set consisting of specific instances of a phenomenon. But instead of concluding with a general statement, the inductive prediction ends with a specific statement about the probability that the next instance will (or will not) have an attribute that will be shared (or not) by the previous instances.  In the example above, although the reasoning process itself is valid, the conclusion is wrong because the premise that there is no drought in the West is false.
A syllogism leads to an incorrect conclusion if one of its sentences is false. Such a syllogism is particularly insidious because it seems very logical – it is indeed logical. But whether wrong or malicious, if any of the above propositions are wrong, then a policy decision based on it (California never has to make plans to deal with a drought) would probably not serve the public interest. Statistical generalizations are also called statistical projections and sample projections.  Inductive reasoning is inherently uncertain. It is only a question of how credible the conclusion is in relation to the premises of a theory of evidence. Examples are multivalued logic, Dempster–Shafer theory, or probability theory with inference rules such as the Bayesian rule. Unlike deductive reasoning, it does not rely on universals that have a closed discursive domain to draw conclusions, so it may also be applicable in cases of epistemic uncertainty (technical problems may arise, however; for example, the second axiom of probability is a closed-world hypothesis).  You have been studying inductive thinking for a very long time.
Inductive reasoning is based on your ability to recognize meaningful patterns and connections. By considering both examples and your understanding of how the world works, induction can help you conclude that something is probably true. You use induction to move from specific data to a generalization that attempts to capture what the data «means.» Inductive reasoning: only probable conclusion Inductive reasoning begins with specific observations of limited scope, and leads to a generalized conclusion that is likely but uncertain given the evidence gathered. You could say that inductive thinking moves from the specific to the general. Much of the scientific research is done using the inductive method: gathering evidence, looking for models, and forming a hypothesis or theory to explain what is being seen. Suppose there are 20 balls – black or white – in an urn. To estimate their respective numbers, take a sample of four spheres and find that three are black and one white. An inductive generalization would be that there are 15 black balls and 5 white balls in the urn. According to Comte, the scientific method formulates predictions, confirms them and formulates laws – positive statements – irrefutable by theology or metaphysics. The British philosopher John Stuart Mill regarded experiment as a justification for enumerative induction by demonstrating the uniformity of nature, welcomed Comte`s positivism but considered scientific laws susceptible to memory or revision, and Mill also withdrew from Comte`s religion of humanity.