#probability — Public Fediverse posts

Icon, Likeness, Likely Story, Likelihood, Probability • 3

Re: Peirce List • Phyllis Chiasson

A more complete excerpt and the translator’s notes are very helpful here.

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss ;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability :  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.1  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.

An enthymeme is a syllogism from probabilities or signs ;  and a sign can be taken in three ways — in just as many ways as there are of taking the middle term in the several figures :  either as in the first figure or as in the second or as in the third.

• E.g., the proof that a woman is pregnant because she has milk is by the first figure ;  for the middle term is ‘having milk’.  A stands for ‘pregnant’, B for ‘having milk’, and C for ‘woman’.
• The proof that the wise are good because Pittacus was good is by the third figure.  A stands for ‘good’, B for ‘the wise’, and C for Pittacus.  Then it is true to predicate both A and B of C ;  only we do not state the latter, because we know it, whereas we formally assume the former.
• The proof that a woman is pregnant because she is sallow is intended to be by the middle figure ;  for since sallowness is a characteristic of woman in pregnancy, and is associated with this particular woman, they suppose that she is proved to be pregnant.  A stands for ‘sallowness’, B for ‘being pregnant’, C for ‘woman’.

If only one premiss is stated, we get only a sign ;  but if the other premiss is assumed as well, we get a syllogism,2 e.g., that Pittacus is high-minded, because those who love honour are high-minded, and Pittacus loves honour ;  or again that the wise are good, because Pittacus is good and also wise.

In this way syllogisms can be effected ;  but whereas a syllogism in the first figure cannot be refuted if it is true, since it is universal, a syllogism in the last figure can be refuted even if the conclusion is true, because the syllogism is neither universal nor relevant to our purpose.3  For if Pittacus is good, it is not necessary for this reason that all other wise men are good.  A syllogism in the middle figure is always and in every way refutable, since we never get a syllogism with the terms in this relation4 ;  for it does not necessarily follow, if a pregnant woman is sallow, and this woman is sallow, that she is pregnant.  Thus truth can be found in all signs, but they differ in the ways which have been described.

We must either classify signs in this way, and regard their middle term as an index (τεκµηριον)5 (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes6 as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics 2.27, 70a3–70b6).

Translator’s Notes

1. If referable to one phenomenon only, a sign has objective necessity ;  if to more than one, its value is a matter of opinion.
2. Strictly an enthymeme.
3. If the signs of an enthymeme in the first figure are true, the conclusion is inevitable.  Aristotle does not mean that the conclusion is universal, but that the universality of the major premiss implies the validity of the minor and conclusion.  The example (<all> those who have honour, etc.) quoted for the third figure contains no universal premiss or sign, and fails to establish a universal conclusion.
4. i.e. when both premisses are affirmative.
5. Signs may be classified as irrefutable (1st figure) and refutable (2nd and 3rd figures), and the name ‘index’ may be attached to their middle terms, either in all figures or (more probably) only in the first, where the middle is distinctively middle.
6. Alternatively the name ‘sign’ may be restricted to the 2nd and 3rd figures, and may be replaced by ‘index’ in the first.

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 3

Re: Peirce List • Phyllis Chiasson

A more complete excerpt and the translator’s notes are very helpful here.

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss ;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability :  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.1  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.

An enthymeme is a syllogism from probabilities or signs ;  and a sign can be taken in three ways — in just as many ways as there are of taking the middle term in the several figures :  either as in the first figure or as in the second or as in the third.

• E.g., the proof that a woman is pregnant because she has milk is by the first figure ;  for the middle term is ‘having milk’.  A stands for ‘pregnant’, B for ‘having milk’, and C for ‘woman’.
• The proof that the wise are good because Pittacus was good is by the third figure.  A stands for ‘good’, B for ‘the wise’, and C for Pittacus.  Then it is true to predicate both A and B of C ;  only we do not state the latter, because we know it, whereas we formally assume the former.
• The proof that a woman is pregnant because she is sallow is intended to be by the middle figure ;  for since sallowness is a characteristic of woman in pregnancy, and is associated with this particular woman, they suppose that she is proved to be pregnant.  A stands for ‘sallowness’, B for ‘being pregnant’, C for ‘woman’.

If only one premiss is stated, we get only a sign ;  but if the other premiss is assumed as well, we get a syllogism,2 e.g., that Pittacus is high-minded, because those who love honour are high-minded, and Pittacus loves honour ;  or again that the wise are good, because Pittacus is good and also wise.

In this way syllogisms can be effected ;  but whereas a syllogism in the first figure cannot be refuted if it is true, since it is universal, a syllogism in the last figure can be refuted even if the conclusion is true, because the syllogism is neither universal nor relevant to our purpose.3  For if Pittacus is good, it is not necessary for this reason that all other wise men are good.  A syllogism in the middle figure is always and in every way refutable, since we never get a syllogism with the terms in this relation4 ;  for it does not necessarily follow, if a pregnant woman is sallow, and this woman is sallow, that she is pregnant.  Thus truth can be found in all signs, but they differ in the ways which have been described.

We must either classify signs in this way, and regard their middle term as an index (τεκµηριον)5 (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes6 as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics 2.27, 70a3–70b6).

Translator’s Notes

1. If referable to one phenomenon only, a sign has objective necessity ;  if to more than one, its value is a matter of opinion.
2. Strictly an enthymeme.
3. If the signs of an enthymeme in the first figure are true, the conclusion is inevitable.  Aristotle does not mean that the conclusion is universal, but that the universality of the major premiss implies the validity of the minor and conclusion.  The example (<all> those who have honour, etc.) quoted for the third figure contains no universal premiss or sign, and fails to establish a universal conclusion.
4. i.e. when both premisses are affirmative.
5. Signs may be classified as irrefutable (1st figure) and refutable (2nd and 3rd figures), and the name ‘index’ may be attached to their middle terms, either in all figures or (more probably) only in the first, where the middle is distinctively middle.
6. Alternatively the name ‘sign’ may be restricted to the 2nd and 3rd figures, and may be replaced by ‘index’ in the first.

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 3

Re: Peirce List • Phyllis Chiasson

A more complete excerpt and the translator’s notes are very helpful here.

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss ;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability :  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.1  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.

An enthymeme is a syllogism from probabilities or signs ;  and a sign can be taken in three ways — in just as many ways as there are of taking the middle term in the several figures :  either as in the first figure or as in the second or as in the third.

• E.g., the proof that a woman is pregnant because she has milk is by the first figure ;  for the middle term is ‘having milk’.  A stands for ‘pregnant’, B for ‘having milk’, and C for ‘woman’.
• The proof that the wise are good because Pittacus was good is by the third figure.  A stands for ‘good’, B for ‘the wise’, and C for Pittacus.  Then it is true to predicate both A and B of C ;  only we do not state the latter, because we know it, whereas we formally assume the former.
• The proof that a woman is pregnant because she is sallow is intended to be by the middle figure ;  for since sallowness is a characteristic of woman in pregnancy, and is associated with this particular woman, they suppose that she is proved to be pregnant.  A stands for ‘sallowness’, B for ‘being pregnant’, C for ‘woman’.

If only one premiss is stated, we get only a sign ;  but if the other premiss is assumed as well, we get a syllogism,2 e.g., that Pittacus is high-minded, because those who love honour are high-minded, and Pittacus loves honour ;  or again that the wise are good, because Pittacus is good and also wise.

In this way syllogisms can be effected ;  but whereas a syllogism in the first figure cannot be refuted if it is true, since it is universal, a syllogism in the last figure can be refuted even if the conclusion is true, because the syllogism is neither universal nor relevant to our purpose.3  For if Pittacus is good, it is not necessary for this reason that all other wise men are good.  A syllogism in the middle figure is always and in every way refutable, since we never get a syllogism with the terms in this relation4 ;  for it does not necessarily follow, if a pregnant woman is sallow, and this woman is sallow, that she is pregnant.  Thus truth can be found in all signs, but they differ in the ways which have been described.

We must either classify signs in this way, and regard their middle term as an index (τεκµηριον)5 (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes6 as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics 2.27, 70a3–70b6).

Translator’s Notes

1. If referable to one phenomenon only, a sign has objective necessity ;  if to more than one, its value is a matter of opinion.
2. Strictly an enthymeme.
3. If the signs of an enthymeme in the first figure are true, the conclusion is inevitable.  Aristotle does not mean that the conclusion is universal, but that the universality of the major premiss implies the validity of the minor and conclusion.  The example (<all> those who have honour, etc.) quoted for the third figure contains no universal premiss or sign, and fails to establish a universal conclusion.
4. i.e. when both premisses are affirmative.
5. Signs may be classified as irrefutable (1st figure) and refutable (2nd and 3rd figures), and the name ‘index’ may be attached to their middle terms, either in all figures or (more probably) only in the first, where the middle is distinctively middle.
6. Alternatively the name ‘sign’ may be restricted to the 2nd and 3rd figures, and may be replaced by ‘index’ in the first.

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 3

Re: Peirce List • Phyllis Chiasson

A more complete excerpt and the translator’s notes are very helpful here.

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss ;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability :  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.1  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.

An enthymeme is a syllogism from probabilities or signs ;  and a sign can be taken in three ways — in just as many ways as there are of taking the middle term in the several figures :  either as in the first figure or as in the second or as in the third.

• E.g., the proof that a woman is pregnant because she has milk is by the first figure ;  for the middle term is ‘having milk’.  A stands for ‘pregnant’, B for ‘having milk’, and C for ‘woman’.
• The proof that the wise are good because Pittacus was good is by the third figure.  A stands for ‘good’, B for ‘the wise’, and C for Pittacus.  Then it is true to predicate both A and B of C ;  only we do not state the latter, because we know it, whereas we formally assume the former.
• The proof that a woman is pregnant because she is sallow is intended to be by the middle figure ;  for since sallowness is a characteristic of woman in pregnancy, and is associated with this particular woman, they suppose that she is proved to be pregnant.  A stands for ‘sallowness’, B for ‘being pregnant’, C for ‘woman’.

If only one premiss is stated, we get only a sign ;  but if the other premiss is assumed as well, we get a syllogism,2 e.g., that Pittacus is high-minded, because those who love honour are high-minded, and Pittacus loves honour ;  or again that the wise are good, because Pittacus is good and also wise.

In this way syllogisms can be effected ;  but whereas a syllogism in the first figure cannot be refuted if it is true, since it is universal, a syllogism in the last figure can be refuted even if the conclusion is true, because the syllogism is neither universal nor relevant to our purpose.3  For if Pittacus is good, it is not necessary for this reason that all other wise men are good.  A syllogism in the middle figure is always and in every way refutable, since we never get a syllogism with the terms in this relation4 ;  for it does not necessarily follow, if a pregnant woman is sallow, and this woman is sallow, that she is pregnant.  Thus truth can be found in all signs, but they differ in the ways which have been described.

We must either classify signs in this way, and regard their middle term as an index (τεκµηριον)5 (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes6 as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics 2.27, 70a3–70b6).

Translator’s Notes

1. If referable to one phenomenon only, a sign has objective necessity ;  if to more than one, its value is a matter of opinion.
2. Strictly an enthymeme.
3. If the signs of an enthymeme in the first figure are true, the conclusion is inevitable.  Aristotle does not mean that the conclusion is universal, but that the universality of the major premiss implies the validity of the minor and conclusion.  The example (<all> those who have honour, etc.) quoted for the third figure contains no universal premiss or sign, and fails to establish a universal conclusion.
4. i.e. when both premisses are affirmative.
5. Signs may be classified as irrefutable (1st figure) and refutable (2nd and 3rd figures), and the name ‘index’ may be attached to their middle terms, either in all figures or (more probably) only in the first, where the middle is distinctively middle.
6. Alternatively the name ‘sign’ may be restricted to the 2nd and 3rd figures, and may be replaced by ‘index’ in the first.

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 3

Re: Peirce List • Phyllis Chiasson

A more complete excerpt and the translator’s notes are very helpful here.

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss ;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability :  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.1  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.

An enthymeme is a syllogism from probabilities or signs ;  and a sign can be taken in three ways — in just as many ways as there are of taking the middle term in the several figures :  either as in the first figure or as in the second or as in the third.

• E.g., the proof that a woman is pregnant because she has milk is by the first figure ;  for the middle term is ‘having milk’.  A stands for ‘pregnant’, B for ‘having milk’, and C for ‘woman’.
• The proof that the wise are good because Pittacus was good is by the third figure.  A stands for ‘good’, B for ‘the wise’, and C for Pittacus.  Then it is true to predicate both A and B of C ;  only we do not state the latter, because we know it, whereas we formally assume the former.
• The proof that a woman is pregnant because she is sallow is intended to be by the middle figure ;  for since sallowness is a characteristic of woman in pregnancy, and is associated with this particular woman, they suppose that she is proved to be pregnant.  A stands for ‘sallowness’, B for ‘being pregnant’, C for ‘woman’.

If only one premiss is stated, we get only a sign ;  but if the other premiss is assumed as well, we get a syllogism,2 e.g., that Pittacus is high-minded, because those who love honour are high-minded, and Pittacus loves honour ;  or again that the wise are good, because Pittacus is good and also wise.

In this way syllogisms can be effected ;  but whereas a syllogism in the first figure cannot be refuted if it is true, since it is universal, a syllogism in the last figure can be refuted even if the conclusion is true, because the syllogism is neither universal nor relevant to our purpose.3  For if Pittacus is good, it is not necessary for this reason that all other wise men are good.  A syllogism in the middle figure is always and in every way refutable, since we never get a syllogism with the terms in this relation4 ;  for it does not necessarily follow, if a pregnant woman is sallow, and this woman is sallow, that she is pregnant.  Thus truth can be found in all signs, but they differ in the ways which have been described.

We must either classify signs in this way, and regard their middle term as an index (τεκµηριον)5 (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes6 as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics 2.27, 70a3–70b6).

Translator’s Notes

1. If referable to one phenomenon only, a sign has objective necessity ;  if to more than one, its value is a matter of opinion.
2. Strictly an enthymeme.
3. If the signs of an enthymeme in the first figure are true, the conclusion is inevitable.  Aristotle does not mean that the conclusion is universal, but that the universality of the major premiss implies the validity of the minor and conclusion.  The example (<all> those who have honour, etc.) quoted for the third figure contains no universal premiss or sign, and fails to establish a universal conclusion.
4. i.e. when both premisses are affirmative.
5. Signs may be classified as irrefutable (1st figure) and refutable (2nd and 3rd figures), and the name ‘index’ may be attached to their middle terms, either in all figures or (more probably) only in the first, where the middle is distinctively middle.
6. Alternatively the name ‘sign’ may be restricted to the 2nd and 3rd figures, and may be replaced by ‘index’ in the first.

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 2
• https://inquiryintoinquiry.com/2026/05/19/icon-likeness-likely-story-likelihood-probability-2-a/

Re: Peirce List • Phyllis Chiasson
• https://web.archive.org/web/20131211153209/http://comments.gmane.org/gmane.science.philosophy.peirce/11234
• https://web.archive.org/web/20131211034001/http://permalink.gmane.org/gmane.science.philosophy.peirce/11235

I'm still a bit fuzzy on how Aristotle's account relates to Peirce's usage, though I'm pretty sure Peirce must have taken Aristotle's usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

❝We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’. For the conclusion which is reached through the first figure is most generally accepted and most true.❞ (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 2
• https://inquiryintoinquiry.com/2026/05/19/icon-likeness-likely-story-likelihood-probability-2-a/

Re: Peirce List • Phyllis Chiasson
• https://web.archive.org/web/20131211153209/http://comments.gmane.org/gmane.science.philosophy.peirce/11234
• https://web.archive.org/web/20131211034001/http://permalink.gmane.org/gmane.science.philosophy.peirce/11235

I'm still a bit fuzzy on how Aristotle's account relates to Peirce's usage, though I'm pretty sure Peirce must have taken Aristotle's usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

❝We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’. For the conclusion which is reached through the first figure is most generally accepted and most true.❞ (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 2
• https://inquiryintoinquiry.com/2026/05/19/icon-likeness-likely-story-likelihood-probability-2-a/

Re: Peirce List • Phyllis Chiasson
• https://web.archive.org/web/20131211153209/http://comments.gmane.org/gmane.science.philosophy.peirce/11234
• https://web.archive.org/web/20131211034001/http://permalink.gmane.org/gmane.science.philosophy.peirce/11235

I'm still a bit fuzzy on how Aristotle's account relates to Peirce's usage, though I'm pretty sure Peirce must have taken Aristotle's usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

❝We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’. For the conclusion which is reached through the first figure is most generally accepted and most true.❞ (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 2
• https://inquiryintoinquiry.com/2026/05/19/icon-likeness-likely-story-likelihood-probability-2-a/

Re: Peirce List • Phyllis Chiasson
• https://web.archive.org/web/20131211153209/http://comments.gmane.org/gmane.science.philosophy.peirce/11234
• https://web.archive.org/web/20131211034001/http://permalink.gmane.org/gmane.science.philosophy.peirce/11235

I'm still a bit fuzzy on how Aristotle's account relates to Peirce's usage, though I'm pretty sure Peirce must have taken Aristotle's usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

❝We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’. For the conclusion which is reached through the first figure is most generally accepted and most true.❞ (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 2
• https://inquiryintoinquiry.com/2026/05/19/icon-likeness-likely-story-likelihood-probability-2-a/

Re: Peirce List • Phyllis Chiasson
• https://web.archive.org/web/20131211153209/http://comments.gmane.org/gmane.science.philosophy.peirce/11234
• https://web.archive.org/web/20131211034001/http://permalink.gmane.org/gmane.science.philosophy.peirce/11235

I'm still a bit fuzzy on how Aristotle's account relates to Peirce's usage, though I'm pretty sure Peirce must have taken Aristotle's usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

❝We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’. For the conclusion which is reached through the first figure is most generally accepted and most true.❞ (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 2

Re: Peirce List • Phyllis Chiasson

I’m still a bit fuzzy on how Aristotle’s account relates to Peirce’s usage, though I’m pretty sure Peirce must have taken Aristotle’s usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 2

Re: Peirce List • Phyllis Chiasson

I’m still a bit fuzzy on how Aristotle’s account relates to Peirce’s usage, though I’m pretty sure Peirce must have taken Aristotle’s usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 2

Re: Peirce List • Phyllis Chiasson

I’m still a bit fuzzy on how Aristotle’s account relates to Peirce’s usage, though I’m pretty sure Peirce must have taken Aristotle’s usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 2

Re: Peirce List • Phyllis Chiasson

I’m still a bit fuzzy on how Aristotle’s account relates to Peirce’s usage, though I’m pretty sure Peirce must have taken Aristotle’s usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 2

Re: Peirce List • Phyllis Chiasson

I’m still a bit fuzzy on how Aristotle’s account relates to Peirce’s usage, though I’m pretty sure Peirce must have taken Aristotle’s usage into account, but it does seem that Aristotle drew some sort of distinction here, using a term “tekmerion” which gets translated as “index” to make the following remark later on in that chapter.

We must either classify signs in this way, and regard their middle term as an index [τεκµηριον] (for the name ‘index’ is given to that which causes us to know, and the middle term is especially of this nature), or describe the arguments drawn from the extremes as ‘signs’, and that which is drawn from the middle as an ‘index’.  For the conclusion which is reached through the first figure is most generally accepted and most true.  (Aristotle, Prior Analytics, 2.27.70b1–6).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

cc: Academia.edu • Cybernetics • Laws of Form • Mathstodon
cc: Research Gate • Structural Modeling • Systems Science • Syscoi

Icon, Likeness, Likely Story, Likelihood, Probability • 1
• https://inquiryintoinquiry.com/2026/05/17/icon-likeness-likely-story-likelihood-probability-1-a/

Here's a likely locus classicus for “icon” in its logical sense —

❝A probability (εικος) is not the same as a sign (σηµειον). The former is a generally accepted premiss; for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:

❝For example, that the envious are malevolent or that those who are loved are affectionate.

❝A sign, however, means a demonstrative premiss which is necessary or generally accepted. That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.❞ (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 1
• https://inquiryintoinquiry.com/2026/05/17/icon-likeness-likely-story-likelihood-probability-1-a/

Here's a likely locus classicus for “icon” in its logical sense —

❝A probability (εικος) is not the same as a sign (σηµειον). The former is a generally accepted premiss; for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:

❝For example, that the envious are malevolent or that those who are loved are affectionate.

❝A sign, however, means a demonstrative premiss which is necessary or generally accepted. That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.❞ (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 1
• https://inquiryintoinquiry.com/2026/05/17/icon-likeness-likely-story-likelihood-probability-1-a/

Here's a likely locus classicus for “icon” in its logical sense —

❝A probability (εικος) is not the same as a sign (σηµειον). The former is a generally accepted premiss; for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:

❝For example, that the envious are malevolent or that those who are loved are affectionate.

❝A sign, however, means a demonstrative premiss which is necessary or generally accepted. That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.❞ (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 1
• https://inquiryintoinquiry.com/2026/05/17/icon-likeness-likely-story-likelihood-probability-1-a/

Here's a likely locus classicus for “icon” in its logical sense —

❝A probability (εικος) is not the same as a sign (σηµειον). The former is a generally accepted premiss; for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:

❝For example, that the envious are malevolent or that those who are loved are affectionate.

❝A sign, however, means a demonstrative premiss which is necessary or generally accepted. That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.❞ (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 1
• https://inquiryintoinquiry.com/2026/05/17/icon-likeness-likely-story-likelihood-probability-1-a/

Here's a likely locus classicus for “icon” in its logical sense —

❝A probability (εικος) is not the same as a sign (σηµειον). The former is a generally accepted premiss; for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:

❝For example, that the envious are malevolent or that those who are loved are affectionate.

❝A sign, however, means a demonstrative premiss which is necessary or generally accepted. That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.❞ (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference —

Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource —

Theme One Program • User Guide • Appendix A
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

#Aristotle #Peirce #IconIndexSymbol #Semiotics #SignRelations
#Logic #Mathematics #Probability #ProbableReasoning #Induction
#Inquiry #Analogy #Likelihood #LikelyStory #Likeness #Morphism

Icon, Likeness, Likely Story, Likelihood, Probability • 1

Here’s a likely locus classicus for “icon” in its logical sense —

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.  (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

Related Discussion

• Peirce List • Benjamin Udell • Michael Shapiro

Icon, Likeness, Likely Story, Likelihood, Probability • 1

Here’s a likely locus classicus for “icon” in its logical sense —

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.  (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

Related Discussion

• Peirce List • Benjamin Udell • Michael Shapiro

Icon, Likeness, Likely Story, Likelihood, Probability • 1

Here’s a likely locus classicus for “icon” in its logical sense —

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.  (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

Related Discussion

• Peirce List • Benjamin Udell • Michael Shapiro

Icon, Likeness, Likely Story, Likelihood, Probability • 1

Here’s a likely locus classicus for “icon” in its logical sense —

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.  (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

Related Discussion

• Peirce List • Benjamin Udell • Michael Shapiro

Icon, Likeness, Likely Story, Likelihood, Probability • 1

Here’s a likely locus classicus for “icon” in its logical sense —

A probability (εικος) is not the same as a sign (σηµειον).  The former is a generally accepted premiss;  for that which people know to happen or not to happen, or to be or not to be, usually in a particular way, is a probability:  e.g., that the envious are malevolent or that those who are loved are affectionate.  A sign, however, means a demonstrative premiss which is necessary or generally accepted.  That which coexists with something else, or before or after whose happening something else has happened, is a sign of that something’s having happened or being.  (Aristotle, Prior Analytics, 2.27.70a3–10).

Reference

• Aristotle, “Prior Analytics”, Hugh Tredennick (trans.), pp. 181–531 in Aristotle, Volume 1, Loeb Classical Library, William Heinemann, London, UK, 1938.

Resource

• Theme One Program • User Guide • Appendix A

Related Discussion

• Peirce List • Benjamin Udell • Michael Shapiro

Alright, future engineers!
**Mutually Exclusive Events:** Can't happen simultaneously.
Ex: Rolling a 1 AND a 2 on a single die. P(A and B) = 0.
Pro-Tip: If ME, P(A or B) = P(A) + P(B). Simple addition!
#Probability #Statistics #STEM #StudyNotes

Alright, future engineers!
The **Expected Value (E[X])** is the long-run average outcome of a random variable.
Ex: For a fair 6-sided die, E[X] = 3.5. (It's `Sum(x * P(x))`).
Pro-Tip: It's a theoretical average, not necessarily an outcome you'll see in a single trial!
#Statistics #Probability #STEM #StudyNotes

#probability : appearance of reality or truth

- French: Probabilité

- German: die Wahrscheinlichkeit

- Italian: probabilità

- Portuguese: probabilidade

- Spanish: probabilidad

------------

Thank you so much for being a member of our community!

Alright, future engineers!
**Probability Mass Function (PMF)**: Gives the probability for *each specific outcome* of a discrete random variable.
Ex: Fair die `P(X=3) = 1/6`.
Pro-Tip: All `P(X=x)` values must be non-negative & sum to 1.
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Probability Mass Function (PMF)**: Gives the probability for *each specific outcome* of a discrete random variable.
Ex: Fair die `P(X=3) = 1/6`.
Pro-Tip: All `P(X=x)` values must be non-negative & sum to 1.
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Permutations**: ways to arrange items where order *matters*.
Ex: Arranging 3 books from 5 on a shelf: `P(5,3) = 5!/2! = 60` ways.
Pro-Tip: Think 'passcodes' or 'rankings' – each sequence is unique!

#Permutations #Probability #STEM #StudyNotes

Alright, future engineers!
**Central Limit Theorem (CLT)**: Sample means of large samples (~30+) are approx. normally distributed, regardless of population distribution.
Ex: Avg of 30 dice rolls will tend towards a normal curve.
Pro-Tip: It's key for statistical inference & confidence intervals!
#Statistics #Probability #STEM #StudyNotes

Alright, future engineers!
**Normal Distribution:** A symmetric, bell-shaped probability distribution, common in natural phenomena.
Ex: Heights or measurement errors often follow this curve.
Pro-Tip: Its properties simplify many statistical analyses, like hypothesis testing!
#Probability #Statistics #STEM #StudyNotes

Alright, future engineers!
**Combinations**: ways to choose items where order *doesn't* matter.
Ex: Choose 2 teammates from 5 friends: `C(5,2) = 5!/(2!3!) = 10` ways.
Pro-Tip: Think 'choosing a committee' – the group is what matters, not selection order!
#Probability #Combinatorics #STEM #StudyNotes

Alright, future engineers!

**Permutations**: ways to arrange items where order matters.
Ex: Arranging 3 books (A,B,C) is 3! = 6 ways.
Pro-Tip: Think 'President, VP, Secretary' - roles are distinct!

#Probability #Combinatorics #STEM #StudyNotes

A Renaissance gambling dispute spawned probability theory

https://www.scientificamerican.com/article/how-a-renaissance-gambling-dispute-spawned-probability-theory/

#HackerNews #Renaissance #Gambling #Probability #Theory #History #Science

@marjolica @Simon318ppm

...whose birthday, incidentally, is today.

Happy birthday, David MacKay! 🎂 🎓 🚀

MacKay was also one of the first to make clear the connection between many machine-learning algorithms, especially neural networks, and Bayesian probability theory: <https://www.inference.org.uk/mackay/PhD.html>.

His book on Information Theory, Inference, and Learning Algorithms is brilliant and full of humour: <https://www.inference.org.uk/itila/book.html>, just like his lectures: <https://videolectures.net/events/course_information_theory_pattern_recognition>.

And just as brilliant is his book "Sustainable Energy – without the hot air" which analyses in a rational way our energy and climate problem: <https://www.withouthotair.com>. See also his TED talk <https://www.ted.com/talks/david_mackay_a_reality_check_on_renewables>.

He died too soon 😢

http://itila.blogspot.com/
https://www.eng.cam.ac.uk/news/professor-sir-david-mackay-1967-2016

#bayesian #probability #MachineLearning #cambridge #physics

@marjolica @Simon318ppm

...whose birthday, incidentally, is today.

Happy birthday, David MacKay! 🎂 🎓 🚀

MacKay was also one of the first to make clear the connection between many machine-learning algorithms, especially neural networks, and Bayesian probability theory: <https://www.inference.org.uk/mackay/PhD.html>.

His book on Information Theory, Inference, and Learning Algorithms is brilliant and full of humour: <https://www.inference.org.uk/itila/book.html>, just like his lectures: <https://videolectures.net/events/course_information_theory_pattern_recognition>.

And just as brilliant is his book "Sustainable Energy – without the hot air" which analyses in a rational way our energy and climate problem: <https://www.withouthotair.com>. See also his TED talk <https://www.ted.com/talks/david_mackay_a_reality_check_on_renewables>.

He died too soon 😢

http://itila.blogspot.com/
https://www.eng.cam.ac.uk/news/professor-sir-david-mackay-1967-2016

#bayesian #probability #MachineLearning #cambridge #physics

@marjolica @Simon318ppm

...whose birthday, incidentally, is today.

Happy birthday, David MacKay! 🎂 🎓 🚀

MacKay was also one of the first to make clear the connection between many machine-learning algorithms, especially neural networks, and Bayesian probability theory: <https://www.inference.org.uk/mackay/PhD.html>.

His book on Information Theory, Inference, and Learning Algorithms is brilliant and full of humour: <https://www.inference.org.uk/itila/book.html>, just like his lectures: <https://videolectures.net/events/course_information_theory_pattern_recognition>.

And just as brilliant is his book "Sustainable Energy – without the hot air" which analyses in a rational way our energy and climate problem: <https://www.withouthotair.com>. See also his TED talk <https://www.ted.com/talks/david_mackay_a_reality_check_on_renewables>.

He died too soon 😢

http://itila.blogspot.com/
https://www.eng.cam.ac.uk/news/professor-sir-david-mackay-1967-2016

#bayesian #probability #MachineLearning #cambridge #physics

@marjolica @Simon318ppm

...whose birthday, incidentally, is today.

Happy birthday, David MacKay! 🎂 🎓 🚀

MacKay was also one of the first to make clear the connection between many machine-learning algorithms, especially neural networks, and Bayesian probability theory: <https://www.inference.org.uk/mackay/PhD.html>.

His book on Information Theory, Inference, and Learning Algorithms is brilliant and full of humour: <https://www.inference.org.uk/itila/book.html>, just like his lectures: <https://videolectures.net/events/course_information_theory_pattern_recognition>.

And just as brilliant is his book "Sustainable Energy – without the hot air" which analyses in a rational way our energy and climate problem: <https://www.withouthotair.com>. See also his TED talk <https://www.ted.com/talks/david_mackay_a_reality_check_on_renewables>.

He died too soon 😢

http://itila.blogspot.com/
https://www.eng.cam.ac.uk/news/professor-sir-david-mackay-1967-2016

#bayesian #probability #MachineLearning #cambridge #physics

@marjolica @Simon318ppm

...whose birthday, incidentally, is today.

Happy birthday, David MacKay! 🎂 🎓 🚀

MacKay was also one of the first to make clear the connection between many machine-learning algorithms, especially neural networks, and Bayesian probability theory: <https://www.inference.org.uk/mackay/PhD.html>.

His book on Information Theory, Inference, and Learning Algorithms is brilliant and full of humour: <https://www.inference.org.uk/itila/book.html>, just like his lectures: <https://videolectures.net/events/course_information_theory_pattern_recognition>.

And just as brilliant is his book "Sustainable Energy – without the hot air" which analyses in a rational way our energy and climate problem: <https://www.withouthotair.com>. See also his TED talk <https://www.ted.com/talks/david_mackay_a_reality_check_on_renewables>.

He died too soon 😢

http://itila.blogspot.com/
https://www.eng.cam.ac.uk/news/professor-sir-david-mackay-1967-2016

#bayesian #probability #MachineLearning #cambridge #physics

Known as the “problem of points,” or “problem of the division of the stakes,” this puzzle stumped mathematicians for more than 150 years. [...] Two greats of 17th-century math, Blaise Pascal and Pierre de Fermat, corresponded about the problem in a famous series of letters. They not only discovered the correct way to share the pot but also created the foundations of modern probability theory in the process."

https://www.scientificamerican.com/article/how-a-renaissance-gambling-dispute-spawned-probability-theory/

#Mathematics #Probability

Alright, future engineers!
**Combinations** count selections where order *doesn't* matter. Ex: Picking 3 teammates from 10. Formula: C(n,r) = n! / (r!(n-r)!). Pro-Tip: If reordering items yields the *same* group, it's a combination!
#Probability #Combinatorics #STEM #StudyNotes

Alright, future engineers!
**Permutations** count arrangements where order *matters*.
Ex: Electing a Pres, VP, Sec from 10 people: P(10,3) = 10!/(10-3)!
Pro-Tip: If switching item order creates a *new* distinct outcome, it's a permutation!
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Permutations** count arrangements where order *matters*.
Ex: Electing a Pres, VP, Sec from 10 people: P(10,3) = 10!/(10-3)!
Pro-Tip: If switching item order creates a *new* distinct outcome, it's a permutation!
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Permutations** count arrangements where order *matters*.
Ex: Electing a Pres, VP, Sec from 10 people: P(10,3) = 10!/(10-3)!
Pro-Tip: If switching item order creates a *new* distinct outcome, it's a permutation!
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Permutations** count arrangements where order *matters*.
Ex: Electing a Pres, VP, Sec from 10 people: P(10,3) = 10!/(10-3)!
Pro-Tip: If switching item order creates a *new* distinct outcome, it's a permutation!
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!
**Permutations** count arrangements where order *matters*.
Ex: Electing a Pres, VP, Sec from 10 people: P(10,3) = 10!/(10-3)!
Pro-Tip: If switching item order creates a *new* distinct outcome, it's a permutation!
#Probability #DiscreteMath #STEM #StudyNotes

Alright, future engineers!

**Expected Value (E[X]):** The long-run average outcome of a random variable. Ex: Fair 6-sided die, E[X] = 3.5. Pro-Tip: Essential for risk assessment & making data-driven decisions!

#Stats #Probability #STEM #StudyNotes

Irreproducibility and Public Trust

A news item from Maynooth University led me to a paper published in Nature with the title Investigating the replicability of the social and behavioural sciences, co-authored by Dermot Lynott, a colleague from the Psychology Department. The abstract reads:

Pursuing replicability — independent evidence for previous claims — is important for creating generalizable knowledge1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2–60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8–54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5–63.1%), although some estimates had high uncertainty. The median Pearson’s r effect size was 0.25 (95% CI 0.21–0.27) for original studies and 0.10 (95% CI 0.09–0.13) for replication studies, an 82.4% (95% CI 67.8–88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social–behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability3,4.

The outcome of this study is that only about half of claimed positive results in such areas as psychology, economics and education were found to be reproducible. The only thing that surpises me about the result is that it is as high as 50%. And before you get snarky about “soft sciences”, there is a similar phenomenon in physics and astronomy too. The Nobel prize-winning physicist John Bahcall famously said that, based on his experience, “about half of all 3σ detections are false”.

I think at least some of the irreproducible results stem from inappropriate statistical reasoning and/or the incorrect interpretation of statistical evidence. I’ve published a number of examples of such things on this blog (e.g. here and here). I also wrote a book some years ago trying to explain the centrality of statistical reasoning to science generally (though from a perspective based on my background in cosmology). I thought I would rehash and publish here some paragraphs from the end of that book that touch on public trust in science. I was fairly optimistic then, but things are undoubtedly far worse now. We’re seeing widespread cuts to research funding in the United States, the UK and many other countries.

–0–

Anyway, I thought I’d take the opportunity to re-iterate why I statistics and statistical reasoning are so important to science. In fact, I think they lie at the very core of the scientific method, although I am still surprised how few practising scientists are comfortable with even basic statistical language. A more important problem is the popular impression that science is about facts and absolute truths. It isn’t. It’s a process. In order to advance it has to question itself. Getting this message wrong – whether by error or on purpose -is immensely dangerous.

Statistical reasoning also applies to many facets of everyday life, including business, commerce, transport, the media, and politics. Probability even plays a role in personal relationships, though mostly at a subconscious level. It is a feature of everyday life that science and technology are deeply embedded in every aspect of what we do each day. Science has given us greater levels of comfort, better health care, and a plethora of labour-saving devices. It has also given us unprecedented ability to destroy the environment and each other, whether through accident or design.

Civilized societies face rigorous challenges in this century. We must confront the threat of climate change and forthcoming energy crises. We must find better ways of resolving conflicts peacefully lest nuclear or conventional weapons lead us to global catastrophe. We must stop large-scale pollution or systematic destruction of the biosphere that nurtures us. And we must do all of these things without abandoning the many positive things that science has brought us. Abandoning science and rationality by retreating into religious or political fundamentalism would be a catastrophe for humanity.

Unfortunately, recent decades have seen a wholesale breakdown of trust between scientists and the public at large. This is due partly to the deliberate abuse of science for immoral purposes, and partly to the sheer carelessness with which various agencies have exploited scientific discoveries without proper evaluation of the risks involved. The abuse of statistical arguments have undoubtedly contributed to the suspicion with which many individuals view science.

There is an increasing alienation between scientists and the general public. Many fewer students enrol for courses in physics and chemistry than a a few decades ago. Fewer graduates mean fewer qualified science teachers in schools. This is a vicious cycle that threatens our future. It must be broken.

The danger is that the decreasing level of understanding of science in society means that knowledge (as well as its consequent power) becomes concentrated in the minds of a few individuals. This could have dire consequences for the future of our democracy.Very few politicians are scientifically literate. How can we expect to control the application of science when the necessary understanding rests with an unelected “priesthood” that is hardly understood by, or represented in, our democratic institutions?

Very few journalists or television producers know enough about science to report sensibly on the latest discoveries or controversies. As a result, important matters that the public needs to know about do not appear at all in the media, or if they do it is in such a garbled fashion that they do more harm than good.

Years ago I used to listen to radio interviews with scientists on the Today programme on BBC Radio 4. I even did such an interview once. It is a deeply frustrating experience. The scientist usually starts by explaining what the discovery is about in the way a scientist should, with careful statements of what is assumed, how the data is interpreted, and what other possible interpretations might be and the likely sources of error. The interviewer then loses patience and asks for a yes or no answer. The scientist tries to continue, but is badgered. Either the interview ends as a row, or the scientist ends up stating a grossly oversimplified version of the story.

Some scientists offer the oversimplified version at the outset, of course, and these are the ones that contribute to the image of scientists as priests. Such individuals often believe in their theories in exactly the same way that some people believe religiously. Not with the conditional and possibly temporary belief that characterizes the scientific method, but with the unquestioning fervour of an unthinking zealot. This approach may pay off for the individual in the short term, in popular esteem and media recognition – but when it goes wrong it is science as a whole that suffers. When a result that has been proclaimed certain is later shown to be false, the result is widespread disillusionment.

The worst example of this tendency that I can think of is the constant use of the phrase “Mind of God” by theoretical physicists to describe fundamental theories. This is not only meaningless but also damaging. As scientists we should know better than to use it. Our theories do not represent absolute truths: they are just the best we can do with the available data and the limited powers of the human mind. We believe in our theories, but only to the extent that we need to accept working hypotheses in order to make progress. Our approach is pragmatic rather than idealistic. We should be humble and avoid making extravagant claims that can’t be justified either theoretically or experimentally.

The more that people get used to the image of “scientist as priest” the more dissatisfied they are with real science. Most of the questions asked of scientists simply can’t be answered with “yes” or “no”. This leaves many with the impression that science is very vague and subjective. The public also tend to lose faith in science when it is unable to come up with quick answers. Science is a process, a way of looking at problems not a list of ready-made answers to impossible problems. Of course it is sometimes vague, but I think it is vague in a rational way and that’s what makes it worthwhile. It is also the reason why science has led to so many objectively measurable advances in our understanding of the world.

I realise I must sound very gloomy about this, but I do think there are good prospects that the gap between science and society may gradually be healed. The fact that the public distrust scientists leads many of them to question us, which is a very good thing. They should question us and we should be prepared to answer them. If they ask us why, we should be prepared to give reasons. If enough scientists engage in this process then what will emerge is and understanding of the enduring value of science. I don’t just mean through the DVD players and computer games science has given us, but through its cultural impact. It is part of human nature to question our place in the Universe, so science is part of what we are. It gives us purpose. But it also shows us a way of living our lives. With some notabnle exceptions, the scientific community is open, internationally-minded, and imbued with a philosophy of cooperation. It values reason and looks to the future rather than the past. Like anyone else, scientists will always make mistakes, but we can always learn from them. The logic of science may not be infallible, but it’s probably the best logic there is in a world so filled with uncertainty.