PARADOXES IN MATHEMATICS

Slides:



Advertisements
Similar presentations
Chapter 7 Hypothesis Testing
Advertisements

Introduction to Proofs
PARADOX Chi-Kwong Li. A ‘ VISUAL ’ PARADOX : I LLUSION.
Practice Quiz 3 Recursive Definitions Relations Basic Counting Pigeonhole Principle Permutations & Combinations Discrete Probability.
Semantic Paradoxes.
Chapter 12 Goodness-of-Fit Tests and Contingency Analysis
Bayes Rule for probability. Let A 1, A 2, …, A k denote a set of events such that An generalization of Bayes Rule for all i and j. Then.
Semantic Paradoxes. THE BARBER The Barber Paradox Once upon a time there was a village, and in this village lived a barber named B.
(CSC 102) Discrete Structures Lecture 14.
For Wednesday, read Chapter 3, section 4. Nongraded Homework: Problems at the end of section 4, set I only; Power of Logic web tutor, 7.4, A, B, and C.
Aim: What are the models of probability?. What is a probability model? Probability Model: a description of a random phenomenon in the language of mathematics.
How likely something is to happen.
Statistics Introduction.
Chapter 14 Analysis of Categorical Data
Today’s Topics n Review Logical Implication & Truth Table Tests for Validity n Truth Value Analysis n Short Form Validity Tests n Consistency and validity.
1 Probability Parts of life are uncertain. Using notions of probability provide a way to deal with the uncertainty.
Saturday May 02 PST 4 PM. Saturday May 02 PST 10:00 PM.
Applying the ideas: Probability
Copyright © Cengage Learning. All rights reserved.
EE1J2 – Discrete Maths Lecture 5 Analysis of arguments (continued) More example proofs Formalisation of arguments in natural language Proof by contradiction.
Chapter 15: Probability Rules
ELEMENTARY NUMBER THEORY AND METHODS OF PROOF
IB Math Studies – Topic 3. IB Course Guide Description.
Psy B07 Chapter 1Slide 1 ANALYSIS OF VARIANCE. Psy B07 Chapter 1Slide 2 t-test refresher  In chapter 7 we talked about analyses that could be conducted.
Key Stone Problem… Key Stone Problem… next Set 22 © 2007 Herbert I. Gross.
All of Statistics Chapter 5: Convergence of Random Variables Nick Schafer.
Chapter 4 Probability 4-1 Overview 4-2 Fundamentals 4-3 Addition Rule
BIOSTATISTICS Topic: Probability 郭士逢 輔大生科系 2007 Note: These slides are made for teaching purpose only, with contents from the textbook, Biostatistics for.
Probability The Basics – Section 4.2.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 10.6.
Surreal Number Tianruo Chen. Introduction In mathematics system, the surreal number system is an arithmetic continuum containing the real number as infinite.
9 February. In Class Assignment #1 Set: {2, 4, 6, 8, 10} MEMBERSUBSETNEITHER 2 {4, 6} {2, 10} {} 3 {{2, 4}, 6, 8, 10}
Warm-up 5.4 Notes Conditional Probability 1.a. b.c.d. e. 2. a. table on the board. Answer b to f in %s since the numbers are really small. b.c. d.e. f.
16.1: Basic Probability. Definitions Probability experiment: An action through which specific results (counts, measurements, or responses) are obtained.
Copyright © 2010 Pearson Education, Inc. Chapter 15 Probability Rules!
Probability Lecture 2. Probability Why did we spend last class talking about probability? How do we use this?
Mathematics Conditional Probability Science and Mathematics Education Research Group Supported by UBC Teaching and Learning Enhancement Fund
Logical Reasoning:Proof Prove the theorem using the basic axioms of algebra.
P(A). Ex 1 11 cards containing the letters of the word PROBABILITY is put in a box. A card is taken out at random. Find the probability that the card.
Week 6 - Friday.  What did we talk about last time?  Solving recurrence relations.
Naïve Set Theory. Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude.
Sixth lecture Concepts of Probabilities. Random Experiment Can be repeated (theoretically) an infinite number of times Has a well-defined set of possible.
7-4: Triangle Inequality Theorem. Theorem 7-9 (Triangle Inequality Theorem): The sum of the measures of any two sides of a triangle is greater than the.
Revision lecture MA30041: Metric Spaces. Just to become familiar with the clicker: What day of the week is today? 1.Sunday 2.Monday 3.Tuesday 4.Wednesday.
1 Hypothesis Testing Basic Problem We are interested in deciding whether some data credits or discredits some “hypothesis” (often a statement about the.
STT 315 This lecture note is based on Chapter 3
1 Chapter 4, Part 1 Basic ideas of Probability Relative Frequency, Classical Probability Compound Events, The Addition Rule Disjoint Events.
Two-Sample Proportions Inference. Sampling Distributions for the difference in proportions When tossing pennies, the probability of the coin landing.
Week 4 - Friday.  What did we talk about last time?  Floor and ceiling  Proof by contradiction.
1 Section 8.2 Basics of Hypothesis Testing Objective For a population parameter (p, µ, σ) we wish to test whether a predicted value is close to the actual.
Introduction to Proofs. The use of Reasoning and Logic in proofs Inductive Reasoning- “reasoning from detailed facts to general principles” – Specific.
Chance Experiments with Outcomes that are not Equally Likely.
English 9A Bellwork Week Seven. MONDAY Write out the sentences below. Underline the verb(s) and place a box around the subject in each sentences. Make.
Reviews of probability Question 1: Suppose we have a “ABCDE” litters how many words we can compose from them with 4 litters without repetition.
Section 1.7. Section Summary Mathematical Proofs Forms of Theorems Direct Proofs Indirect Proofs Proof of the Contrapositive Proof by Contradiction.
AP Statistics From Randomness to Probability Chapter 14.
PART OF SPEECH DEFINITION SYNONYM SENTENCE NAME TEACHER AND DATE1 WORDS OF THE WEEK.
PART OF SPEECH DEFINITION SYNONYM SENTENCE NAME TEACHER AND DATE1 WORDS OF THE WEEK.
Lecture #8 Thursday, September 15, 2016 Textbook: Section 4.4
The Pigeonhole Principle
Example 1A: Using the Fundamental Counting Principle
Jeffrey Martinez Math 170 Dr. Lipika Deka 10/15/13
Distinguish valid from invalid arguments and sound from unsound
Semantic Paradoxes.
More Applications of the Pumping Lemma
Testing Hypotheses about a Population Proportion
Mathematical Induction
Propositional Logic 1) Introduction Copyright 2008, Scott Gray.
If there is any case in which true premises lead to a false conclusion, the argument is invalid. Therefore this argument is INVALID.
If there is any case in which true premises lead to a false conclusion, the argument is invalid. Therefore this argument is INVALID.
Presentation transcript:

PARADOXES IN MATHEMATICS

WHAT IS A PARADOX? A paradox is an argument that produces an inconsistency within logic. Most logical paradoxes are known to be invalid arguments however there are exceptions.

A few TYPES OF PARADOXES SELF-REFERENCE CONTRADICTION VICIOUS CIRCULARITY ALSO KNOWN AS INFINITE REGRESS

SOME FAMILIAR EXAMPLES

SELF-REFERENCE PARADOX THE BARBER PARADOX IS THE ANSWER TO THIS QUESTION NO?

CONTRADICTION PARADOX THIS STATEMENT IS FALSE NOTHING IS IMPOSSIBLE

VICIOUS CIRCULARITY PARADOX “THE FOLLOWING SENTENCE IS TRUE” “THE PREVIOUS SENTENCE IS FALSE” WHAT HAPPENS WHEN PINOCCHIO SAYS “MY NOSE WILL GROW NOW?”

Why use paradoxes in math? They are good in promoting critical thinking . Some paradoxes have revealed errors in definitions assumed to be rigorous, and have caused axioms of mathematics and logic to be re-examined. One example is Russell’s paradox.

TRHEE FAMOUS PARADOXES IN PROBABILITY THE THREE DOGS PARADOX BERTRAND’S BOX PARADOX THE TWO DOGS PARADOX

THE THREE DOGS PARADOX There are three dogs, equally likely to be male or female. If one of them is male, what is the probability that all of them are male? are male?

PROBABILITY OF EACH DOG to be male D1= 1/2 (male or female) D2= 1/2 (male or female) D3= 1/2 (male or female) But we are told that one of the dogs is male, say D1, so we are left with D2 and D3 to be males. so now we only have D1 and D2 unknown. D2= 1/2 D3=1/2

From here, can we say that since the probability D2 and D3 to be both males is (1/2)(1/2)= 1/4 therefore the answer is 1/4?

ANOTHER APPROACH Let S be a sample space for the three dogs, then. S={MMM, MMF, MFM,FMM, FFM, FMF, MFF} Note: {FFF} possibility is ruled out since we know that at least one of the dogs is male. Looking at the sample space we created. What is the probability of having all three dogs be male?

Conclusion for the three dogs paradox The probability for 3 dogs to be male is (1/8) The probability for 2 dogs to be male is (1/4) The probability for 3 dogs to be male given that one is male is (1/7). Hence the paradox.

BERTRAND’S BOX PARADOX There are 3 jewelry boxes. Each box has 2 drawers with one gemstone in each drawer as follows. B1- Diamond, Diamond B2- Diamond, Emerald B3- Emerald, Emerald One box is chosen at random and one of its drawers opened. If a diamond is found what is the probability that the other drawer of this box has the other diamond?

ATTEMPT TO THE SOLUTION Let B=Box, D=Diamond, E= Emerald B1- D,D B2- D,E B3- E,E Since we are looking for the P(D,D) and we know we found one diamond, the B-(E,E) option is ruled out. We have left B1 and B2 therefore it seems like we have a 50% chance on each of the boxes because we could easily have the (D,E) or the (D,D), right?

A DIFFERENT ATTEMPT Let’s assign individual probabilities to each jewelry box. Using probability notation to get a diamond on each box. If we have the (D,D) box the P(D,D)= 1 If we have the (D,E) box the P(D,E)=1/2 if we have the (E,E) box the P(D,E) =0 If we want the probability of the 2 diamonds on the same box, we must use the diamond path by Bayes theorem. P(D,D)= [P(D,D)*P(B1)] / [P(D,D)*P(B1)+P(D,E)*P(B2)+P(E,E)*P(B3)]

Picture approach Box Diamond Found EMERALD

Probability finding a second diamond Found P(0) P(1/3) P(0)

LAST BUT NOT LEAST

The two dogs paradox There are two dogs. One of them is male and was born on Sunday. What is the probability that the other dog is male? Assume that male and female dogs are equally likely to be born.

We have two dogs, and we know that one is a male We have two dogs, and we know that one is a male. We know they are equally likely to be born male and female. We have one more dog, so this dog can be male or female which give us the answer that the probability that this dog is male is .5 = 1/2. The problem also mentions that the dog was born on Sunday. Should that change anything? Let’s see.

A MORE DETAILED LOOK AT THE PROBLEM We have two genders, Male and Female. We have Seven days of the week, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday and Sunday. If we create a sample space for the two dogs we would have something like: S={SMSM, SMSF, SFSM,SMMF,SFMM,….,SFSF} In total our sample space would be 14^(2)=196 paired by gender/day. But this configuration includes all possibilities, so we need to take some out.

Lets take out the samples that do not feature a male born on Sunday. Female/Female=49 Non Sunday male/Non Sunday male=36 Female/Non Sunday male =42 Non Sunday male/Female =42 49+36+42+42= 169 This gives us 169 outcomes that we can rule out since these don’t have a male born on Sunday. We have 196-169= 27 samples with a male born on Sunday. These could be our possibilities.

Now we can count the samples with one male dog born on Sunday. Sunday male/Non Sunday male =6 Non Sunday male/Sunday male=6 Sunday male/Female =7 Female/Sunday male=7 Sunday male/Sunday male=1 The probability that the second dog is male is produced by counting the days in which the two dogs where male.

The tree samples in which the two dogs were male given that at least one was born on Sunday are: Sunday male/Non Sunday male =6 Non Sunday male/Sunday male=6 Sunday male/Sunday male=1 We have 6+6+1=13 Since our possibilities were 27 we get the final result (13/27)< (1/2) Hence it matters that a dog is born on Sunday, Hence the paradox.