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Posterior Analytics

Posterior Analytics is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Posterior Analytics rather than just read about it. In short: The Posterior Analytics (Ancient Greek: Ἀναλυτικὰ Ὕστερα; Latin: Analytica Posteriora) is a text from Aristotle's Organon that deals with demonstration, definition, and scientific knowledge. The demonstration is distinguished as a syllogism productive of scientific knowledge, while the definition marked as the statement of a thing's nature, ... a statement of the meaning of the name, or of an equivalent nominal form…

Key takeaways

  • Posterior Analytics belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Posterior Analytics to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Posterior Analytics from memory before moving on to harder problems.

Reference excerpt

The Posterior Analytics (Ancient Greek: Ἀναλυτικὰ Ὕστερα; Latin: Analytica Posteriora) is a text from Aristotle's Organon that deals with demonstration, definition, and scientific knowledge. The demonstration is distinguished as a syllogism productive of scientific knowledge, while the definition marked as the statement of a thing's nature, ... a statement of the meaning of the name, or of an equivalent nominal formula.

Content In the Prior Analytics, syllogistic logic is considered in its formal aspect; in the Posterior it is considered in respect of its matter. The "form" of a syllogism lies in the necessary connection between the premises and the conclusion. Even where there is no fault in the form, there may be in the matter, i.e. the propositions of which it is composed, which may be true or false, probable or improbable. When the premises are certain, true, and primary, and the conclusion formally follows from them, this is demonstration, and produces scientific knowledge of a thing. Such syllogisms are called apodeictical, and are dealt with in the two books of the Posterior Analytics. When the premises are not certain, such a syllogism is called dialectical, and these are dealt with in the eight books of the Topics. A syllogism which seems to be perfect both in matter and form, but which is not, is called sophistical, and these are dealt with in the book On Sophistical Refutations. The contents of the Posterior Analytics may be summarised as follows:

All demonstration must be founded on principles already known. The principles on which it is founded must either themselves be demonstrable, or be so-called first principles, which cannot be demonstrated, nor need to be, being evident in themselves ("nota per se"). We cannot demonstrate things in a circular way, supporting the conclusion by the premises, and the premises by the conclusion. Nor can there be an infinite number of middle terms between the first principle and the conclusion. In all demonstration, the first principles, the conclusion, and all the intermediate propositions, must be necessary, general and eternal truths. Of things that happen by chance, or contingently, or which can change, or of individual things, there is no demonstration. Some demonstrations prove only that the things are a certain way, rather than why they are so. The latter are the most perfect. The first figure of the syllogism (see term logic for an outline of syllogistic theory) is best adapted to demonstration, because it affords conclusions universally affirmative. This figure is commonly used by mathematicians. The demonstration of an affirmative proposition is preferable to that of a negative; the demonstration of a universal to that of a particular; and direct demonstration to a reductio ad absurdum. The principles are more certain than the conclusion. There cannot be both opinion and knowledge of the same thing at the same time. In the second book, Aristotle starts with a remarkable statement, the kinds of things determine the kinds of questions, which are four:

Whether the relation of a property (attribute) with a thing is a true fact (τὸ ὅτι). What is the reason of this connection (τὸ διότι). Whether a thing exists (εἰ ἔστι). What is the nature and meaning of the thing (τί ἐστιν). Or in a more literal translation (Owen): 1. that a thing is, 2. why it is, 3. if it is, 4. what it is. The last of these questions was called by Aristotle, in Greek, the "what it is" of a thing. Scholastic logicians translated this into Latin as "quiddity" (quidditas). This quiddity cannot be demonstrated, but must be fixed by a definition. He deals with definition, and how a correct definition should be made. As an example, he gives a definition of the number three, defining it to be the first odd prime number. Maintaining that "to know a thing's nature is to know the reason why it is" and "we possess scientific knowledge of a thing only when we know its cause", Aristotle posited four major sorts of cause as the most sought-after middle terms of demonstration: the definable form; an antecedent which necessitates a consequent; the efficient cause; the final cause. He concludes the book with the way the human mind comes to know the basic truths or primary premises or first principles, which are not innate, because people may be ignorant of them for much of their lives. Nor can they be deduced from any previous knowledge, or they would not be first principles. He states that first principles are derived by induction, from the sense-perception implanting the true universals in the human mind. From this idea comes the scholastic maxim "there is nothing in the understanding which was not prior in the senses". Of all types of thinking, scientific knowing and intuition are considered as only universally true, where the latter is the originative source of scientific knowledge.

References Aristotle, Analytica Priora et Posteriora. Ed. Ross and Minio-Paluello. Oxford University Press, 1981. ISBN 9780198145622. Greek text. Aristotle, Posterior Analytics; Topica. Greek text with translation by Hugh Tredennick, E. S. Forster. Loeb Classical Library 391. Cambridge, MA: Harvard University Press, 1960. Aristotle (2007), Posterior Analytics, translated by Mure, G. R. G., The University of Adelaide: eBooks @ Adelaide, archived from the original on 2007-04-27.

External links

Ἀναλυτικὰ ὕστερα, at Bibliotheca Augustana Posterior Analytics, trans. by Mure, G. R. G. at Logic Museum Posterior Analytics, trans. by Octavius Freire Owen Public domain audiobook version of Posterior Analytics, trans. by Octavius Freire Owen Posterior Analytics public domain audiobook at LibriVox Text of Posterior Analytics, (in html, epub or mobi format) as translated by G. R. G. Mure

Worked examples

Example 1 — a first encounter with Posterior Analytics

Start with the simplest possible case. Write down what Posterior Analytics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Posterior Analytics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Posterior Analytics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Posterior Analytics

In research
Posterior Analytics appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Posterior Analytics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Posterior Analytics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Logic literature, Works by Aristotle, so understanding it makes those chapters shorter.
In everyday life
Look for Posterior Analytics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Posterior Analytics in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Posterior Analytics means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Posterior Analytics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Posterior Analytics in simple terms?

The Posterior Analytics (Ancient Greek: Ἀναλυτικὰ Ὕστερα; Latin: Analytica Posteriora) is a text from Aristotle's Organon that deals with demonstration, definition, and scientific knowledge. The demonstration is distinguished as a syllogism productive of scientific knowledge, while the definition m…

Why does Posterior Analytics matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Posterior Analytics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Posterior Analytics.

Tags

  • Logic literature
  • Works by Aristotle

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