# Inductive and deductive approach - rectoria.unal.edu.co

inductive and deductive approach.

Inductive reasoning is a method of reasoning in which the premises inductive and deductive approach viewed as supplying some evidence, but not full assurance, of the truth inducyive the conclusion. Inductive reasoning is distinct from deductive reasoning. If the premises are correct, the conclusion of a deductive argument is certain ; in contrast, the truth of the conclusion of an inductive argument is probablebased upon the evidence given.

A generalization inductive and deductive approach accurately, an inductive generalization proceeds from a premise about a sample to a conclusion about the population. For example, say there are 20 balls—either aproach or white—in an urn. To estimate their respective numbers, you draw a sample of four balls and find that three are black and one is white. An inductive generalization would be that there are 15 black and 5 white balls in the urn. How much the premises support the conclusion depends upon 1 the number in the sample group, 2 the more info in the population, and 3 the degree to which the sample represents the population which may be achieved by taking a random sample.

The hasty generalization and the biased sample are generalization fallacies.

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A statistical generalization is a type of inductive argument in which a conclusion about a population is inferred using a statistically-representative sample. For example:. The measure is highly reliable within a well-defined margin of error provided the sample is large and random. It is readily quantifiable.

Compare the preceding argument with the following. Statistical generalizations are also called statistical projections [6] and sample projections.

## Inductive Approaches and Some Examples

An anecdotal generalization apprkach a type of inductive argument in which a conclusion about a click here is inferred using a non-statistical sample. This inference is less reliable and thus more likely to commit the fallacy of hasty generalization than a statistical generalization, first, because the sample events are non-random, and second because it is not reducible to mathematical expression. Statistically speaking, there is simply no way to know, measure and calculate as to the circumstances affecting performance that will obtain in the future. On a philosophical level, the argument relies on the presupposition that the operation of future events will mirror the past. In other words, it takes for granted a uniformity inductive and deductive approach nature, an unproven principle that cannot be derived from the empirical data itself.

Arguments that tacitly presuppose this uniformity are sometimes called Humean after the philosopher who was first to subject them to philosophical scrutiny. An inductive prediction draws a conclusion about a future instance from a past and current sample. Like an inductive generalization, an inductive prediction typically relies on a data set consisting of specific instances of a phenomenon.]

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