Probability Fundamentals & Geometric Probability

Study Sheet

Probability Fundamentals & Geometric Probability

Competition counting & probability, Unit 7

Sample Spaces & Equally Likely Outcomes

Concept
What we are counting

An experiment is any process with an uncertain result. The sample space SS is the set of all possible outcomes, and an event EE is any subset of SS (a collection of outcomes we care about).

When every outcome in SS is equally likely, probability becomes pure counting:

P(E)=number of favorable outcomesnumber of total outcomes=ES.P(E) = \frac{\text{number of favorable outcomes}}{\text{number of total outcomes}} = \frac{|E|}{|S|}.

The single most important habit is to build a sample space in which the outcomes really are equally likely --- otherwise the formula does not apply.

Example
Worked example: two dice, equally likely outcomes

Two fair dice are rolled. What is the sample space, and how likely is the sum 77?

Solution. List ordered pairs (a,b)(a,b) so each is equally likely: S=66=36|S| = 6 \cdot 6 = 36. The sum is 77 for (1,6),(2,5),(3,4),(4,3),(5,2),(6,1)(1,6),(2,5),(3,4),(4,3),(5,2),(6,1), so E=6|E| = 6 and

P(sum=7)=636=16.P(\text{sum}=7) = \frac{6}{36} = \frac{1}{6}.

If instead we had used the 1111 possible sums 2,3,,122,3,\dots,12 as our sample space, those outcomes are not equally likely, and the counting formula would give a wrong answer.

Tip

Pitfall. “Number of things that can happen” is only a valid denominator when those things are equally likely. Prefer ordered, atomic outcomes (each coin, each die, each draw distinguished) to force equal likelihood.

Probability as Favorable over Total (Counting-Based)

Concept
Probability is a counting ratio

Once outcomes are equally likely, every probability question is two counting questions: count E|E| (favorable) and count S|S| (total), then divide. Basic properties follow immediately:

0P(E)1,P()=0,P(S)=1.0 \le P(E) \le 1, \qquad P(\varnothing) = 0, \qquad P(S) = 1.

For disjoint (mutually exclusive) events, probabilities add: P(AB)=P(A)+P(B)P(A \cup B) = P(A) + P(B). In general, inclusion--exclusion gives P(AB)=P(A)+P(B)P(AB)P(A\cup B) = P(A)+P(B)-P(A\cap B).

Example
Worked example: drawing a card

One card is drawn from a standard 5252-card deck. Find P(a face card or a heart)P(\text{a face card or a heart}).

Solution. Face cards: 1212. Hearts: 1313. Overlap (face-card hearts): 33. By inclusion--exclusion the favorable count is 12+133=2212 + 13 - 3 = 22, so

P=2252=1126.P = \frac{22}{52} = \frac{11}{26}.
Example
Worked example: reduce every answer

A jar has 44 red and 66 blue marbles. One marble is drawn. Find P(red)P(\text{red}).

Solution. P(red)=410=25P(\text{red}) = \dfrac{4}{10} = \dfrac{2}{5}. Contest answers are almost always expected as reduced fractions.

Tip

Tip. Keep the same “kind” of object in numerator and denominator. If you count the total as ordered outcomes, count the favorable as ordered too; if unordered, unordered. Mixing the two is the classic source of wrong probabilities.

Complementary Probability

Concept
Count the opposite instead

The complement EcE^c is the event “EE does not happen.” Since EE and EcE^c partition SS,

P(E)+P(Ec)=1P(E)=1P(Ec).P(E) + P(E^c) = 1 \qquad\Longrightarrow\qquad P(E) = 1 - P(E^c).

Reach for the complement whenever EE is described by “at least one,” “at least,” or “not all” --- the opposite event (“none,” “exactly zero”) is usually far easier to count.

Example
Worked example: at least one head

A fair coin is flipped 55 times. Find P(at least one head)P(\text{at least one head}).

Solution. The complement is “no heads,” i.e. all 55 tails, with probability (12)5=132\left(\tfrac12\right)^5 = \tfrac{1}{32}. Hence

P(at least one head)=1132=3132.P(\text{at least one head}) = 1 - \frac{1}{32} = \frac{31}{32}.

Counting the 55 separate “exactly kk heads” cases would be far more work.

Example
Worked example: shared birthday flavor

Three people each pick a day of the week at random. Find P(at least two pick the same day)P(\text{at least two pick the same day}).

Solution. Complement: all three days distinct. Total =73=343= 7^3 = 343; all-distinct =765=210= 7\cdot 6\cdot 5 = 210. So

P(some match)=1210343=13049=1949.P(\text{some match}) = 1 - \frac{210}{343} = 1 - \frac{30}{49} = \frac{19}{49}.
Tip

Tip. “At least one” \Rightarrow subtract “none.” The complement trick converts an OR of many cases into a single, clean count.

Probability with Permutations & Combinations

Concept
Counting tools power the ratio

For selection problems, S|S| and E|E| are themselves permutation or combination counts. Decide once whether order matters and use the same convention on top and bottom:

P(E)=favorable arrangements/selectionstotal arrangements/selections,(nk)=n!k!(nk)!,P(n,k)=n!(nk)!.P(E) = \frac{\text{favorable arrangements/selections}}{\text{total arrangements/selections}}, \quad \binom{n}{k}=\frac{n!}{k!\,(n-k)!}, \quad P(n,k)=\frac{n!}{(n-k)!}.

For committees, hands, and unordered draws, ()\binom{}{} appears in both numerator and denominator.

Example
Worked example: a committee count

A club of 66 boys and 44 girls chooses a 33-person committee at random. Find P(exactly 2 girls)P(\text{exactly } 2 \text{ girls}).

Solution. Total committees: (103)=120\binom{10}{3}=120. Favorable: choose 22 of 44 girls and 11 of 66 boys, (42)(61)=66=36\binom{4}{2}\binom{6}{1}=6\cdot 6=36. So

P=36120=310.P = \frac{36}{120} = \frac{3}{10}.
Example
Worked example: a poker-style hand

Five cards are dealt from a standard deck. Find P(all five are hearts)P(\text{all five are hearts}).

Solution. Total 55-card hands: (525)\binom{52}{5}. All-heart hands: (135)\binom{13}{5}. So

P=(135)(525)=12872,598,960=3366640.P = \frac{\binom{13}{5}}{\binom{52}{5}} = \frac{1287}{2{,}598{,}960} = \frac{33}{66640}.

Order is irrelevant to a hand, so combinations appear top and bottom.

Tip

Pitfall. Never mix ordered and unordered counts. Using ()\binom{}{} on top but P(n,k)P(n,k) (or n!n!) on the bottom silently multiplies your answer by a stray factorial.

Independent vs. Dependent Events (Basic)

Concept
Does one event change the other?

Events AA and BB are independent if knowing one occurred does not change the probability of the other. Then

P(AB)=P(A)P(B).P(A \cap B) = P(A)\,P(B).

If the events are dependent, use the multiplication rule with a conditional probability:

P(AB)=P(A)P(BA),P(A \cap B) = P(A)\cdot P(B \mid A),

where P(BA)P(B\mid A) is computed after AA has happened. Drawing with replacement keeps events independent; drawing without replacement makes them dependent.

Example
Worked example: independent (with replacement)

A bag has 33 red and 55 green marbles. Two are drawn with replacement. Find P(both red)P(\text{both red}).

Solution. Replacement restores the bag, so the draws are independent, each with P(red)=38P(\text{red})=\tfrac{3}{8}:

P(both red)=3838=964.P(\text{both red}) = \frac{3}{8}\cdot\frac{3}{8} = \frac{9}{64}.
Example
Worked example: dependent (without replacement)

Same bag (33 red, 55 green), but the two marbles are drawn without replacement. Find P(both red)P(\text{both red}).

Solution. The second draw depends on the first:

P(both red)=3827=656=328.P(\text{both red}) = \frac{3}{8}\cdot\frac{2}{7} = \frac{6}{56} = \frac{3}{28}.

Fewer red marbles and fewer total marbles remain, so the second factor shrinks.

Tip

Tip. “With replacement” \Rightarrow independent \Rightarrow multiply unchanged probabilities. “Without replacement” \Rightarrow dependent \Rightarrow update the counts after each draw. When in doubt, count the whole thing with combinations instead.

Geometric Probability (Length & Area)

Concept
Probability as a ratio of measures

When an outcome is a uniformly random point in a region --- a spot on a segment, an instant in a time interval, a dart in a target --- the sample space is continuous and we replace counting with measure:

P(E)=length of favorable intervallength of whole intervalorarea of favorable regionarea of whole region.P(E) = \frac{\text{length of favorable interval}}{\text{length of whole interval}} \quad\text{or}\quad \frac{\text{area of favorable region}}{\text{area of whole region}}.

The idea is identical to ES\tfrac{|E|}{|S|}: favorable “size” over total “size,” now measured by length or area instead of by count.

Example
Worked example: dart in a square

A dart lands at a uniformly random point in a 4×44\times 4 square. Find the probability it lands inside the inscribed circle of radius 22 (figure above).

Solution. Favorable area =π(2)2=4π=\pi(2)^2 = 4\pi; total area =16=16. So

P=4π16=π40.785.P = \frac{4\pi}{16} = \frac{\pi}{4} \approx 0.785.

Geometric probabilities need not be rational --- they inherit whatever constants (π\pi, radicals) the geometry contains.

Example
Worked example: a random point on a segment

A point XX is chosen uniformly on the segment from 00 to 1212. Find P(X>9)P(X > 9).

Solution. Favorable length =129=3= 12 - 9 = 3; total length =12= 12. So P=312=14.P = \dfrac{3}{12} = \dfrac{1}{4}.

Tip

Tip. Match dimensions: a one-variable (single random point) problem uses length; a two-variable (two independent random points, or a point in the plane) problem uses area. Sketch the favorable region before computing.

Going Deeper: Why Probability Is Counting, and the Meeting Problem

Concept
Equally likely outcomes make probability into counting

The formula P(E)=E/SP(E)=|E|/|S| is not a definition of probability in general --- it is a theorem about the special case of equally likely outcomes. Each of the S|S| atomic outcomes carries probability 1S\tfrac{1}{|S|}, and an event's probability is the sum of the probabilities of the outcomes it contains, which is exactly E1S|E|\cdot\tfrac{1}{|S|}. This is why all of Units 1--6 (permutations, combinations, casework, complementary counting, inclusion--exclusion) are really probability tools in disguise: master the count and the probability is one division away.

Geometric probability is the same statement pushed to a continuum. When outcomes are a uniformly random point, no single point can carry positive probability, so instead of counting points we measure events by their size --- length, area, or volume. “Favorable size over total size” plays exactly the role that “favorable count over total count” played, and uniform randomness is precisely what makes that swap legitimate.

Example
Worked example: the meeting problem (area method)

Two friends each arrive at the café at a uniformly random time between 12:00 and 1:00, independently, and each waits 1515 minutes. Find the probability that they meet.

Solution. Let x,y[0,60]x,y \in [0,60] be their arrival minutes. The sample space is the 60×6060\times 60 square, area 36003600. They meet exactly when xy15|x-y|\le 15, the band between the lines y=x+15y = x+15 and y=x15y = x-15. The two failure corners are right triangles with legs 6015=4560-15 = 45, so the failure area is 212452=452=20252\cdot\tfrac12\cdot 45^2 = 45^2 = 2025. Thus

P(meet)=120253600=1916=716.P(\text{meet}) = 1 - \frac{2025}{3600} = 1 - \frac{9}{16} = \frac{7}{16}.

Two independent random times \Rightarrow a point in a square \Rightarrow area ratio. This “two random reals” setup is the signature of area-based geometric probability.

Example
Worked example: uniform points and a broken stick

A stick of length 11 is broken at a uniformly random point. What is the probability the longer piece is at least twice the shorter?

Solution. Let the break be at X[0,1]X\in[0,1]. The longer piece is at least twice the shorter exactly when the shorter piece is at most 13\tfrac13, i.e. X13X\le\tfrac13 or X23X\ge\tfrac23. Favorable length =13+13=23=\tfrac13+\tfrac13=\tfrac23, so P=23P=\dfrac{2}{3}. One random point \Rightarrow length ratio.

Tip

Big picture --- one idea, three costumes. Every probability in this unit is “favorable size ÷\div total size,” and the whole skill is choosing the right notion of size:

  • 2pt
  • Finite, equally likely outcomes \Rightarrow size == count. Use permutations/combinations for E|E| and S|S|; keep ordered-vs-unordered consistent top and bottom.
  • “At least one” / messy OR \Rightarrow count the complement and subtract from 11.
  • Sequential draws \Rightarrow ask independent (multiply, unchanged) or dependent (multiply with updated counts).
  • Uniform random point(s) \Rightarrow size == length (one variable) or area (two variables). Sketch the favorable region; use the complement when the failure region is the simpler shape.

Get the sample space right --- equally likely, or uniform --- and every problem collapses to a single ratio.

Symmetry & Bijection Arguments

Concept
Let the problem's symmetry do the counting

Often the cleanest probability argument computes nothing: instead you observe that several outcomes must, by symmetry, be equally likely, and read the answer off directly.

  • 2pt
  • Random permutations. If nn distinct objects are placed in a uniformly random order, every one of the n!n! orderings is equally likely. Consequently any statement of the form “object AA comes before object BB” holds in exactly half of the orderings, so P(A before B)=12P(A\text{ before }B)=\tfrac12 --- no matter how many other objects there are.
  • Symmetric roles. If kk specific items are equally likely to occupy any of nn positions, then the item in a fixed position (“the first card,” “the top of the deck”) is a uniformly random one of the nn items.
  • Bijections. To show two events are equally likely, exhibit an explicit one-to-one correspondence between their favorable outcomes. Equal-size favorable sets over the same sample space \Rightarrow equal probability.

The payoff: a symmetry or bijection argument replaces a hard count with a one-line observation.

Example
Worked example: relative order in a shuffle (AIME flavor)

A standard 5252-card deck is shuffled uniformly at random. Find the probability that the ace of spades appears somewhere above the king of hearts, and that both appear above the two of clubs.

Solution. Ignore all 4949 other cards: by symmetry the three named cards appear among themselves in each of the 3!=63!=6 relative orders equally likely. Exactly one of those orders is “ace of spades, then king of hearts, then two of clubs.” Hence

P=13!=16.P = \frac{1}{3!} = \frac{1}{6}.

The 4949 irrelevant cards never enter the computation --- symmetry collapses the whole shuffle to the relative order of the three cards we care about.

Example
Worked example: a bijection between draws

From a bag of chips numbered 11 through nn, two distinct chips are drawn. Show that the smaller number is equally likely to be any value that leaves room for a larger partner, and use symmetry to find P(the two numbers differ by exactly 1)P(\text{the two numbers differ by exactly }1) for n=10n=10.

Solution. Total unordered pairs: (102)=45\binom{10}{2}=45, all equally likely. Consecutive pairs are (1,2),(2,3),,(9,10)(1,2),(2,3),\dots,(9,10): there are 99 of them. So P=945=15P=\dfrac{9}{45}=\dfrac{1}{5}. The bijection “pair (k,k+1)k(k,k+1)\leftrightarrow k” shows there are exactly n1n-1 consecutive pairs, giving the general answer n1(n2)=2n\dfrac{n-1}{\binom{n}{2}}=\dfrac{2}{n}.

Tip

Tip. Before counting, ask “are these outcomes interchangeable?” If swapping two objects (or reversing an order) maps favorable outcomes to favorable outcomes and unfavorable to unfavorable, you have a symmetry --- use it. “AA before BB” in a random order is the workhorse: its probability is always 12\tfrac12.

Higher-Dimensional Geometric Probability

Concept
Pick the right measure: length, area, or volume

Uniform random points still give P(E)=measure of favorable regionmeasure of whole regionP(E) = \dfrac{\text{measure of favorable region}}{\text{measure of whole region}}, but in 22 or 33 variables the “measure” is an area or a volume.

  • 2pt
  • Use a ratio of measures when the favorable region is a recognizable shape (triangle, disk, polygon, box) whose area or volume you can find by geometry. This is the first thing to try.
  • Cut, complement, or exploit symmetry when the region is awkward: split it into triangles and rectangles, measure the failure region instead, or find a rigid motion (like swapping coordinates) that pairs the region with one you already know.

Three independent uniform reals \Rightarrow a point in a cube \Rightarrow volume ratio; a curved boundary \Rightarrow reach for a known circle or sphere formula.

Example
Worked example: a probability is a ratio, an average comes from symmetry

A point (x,y)(x,y) is chosen uniformly in the unit square [0,1]2[0,1]^2.

(a) Find P ⁣(x+y12)P\!\left(x+y \le \tfrac12\right).   (b) Find the expected value of x+yx+y.

Solution. (a) The whole square has area 11. The favorable set x+y12x+y\le\tfrac12 is a right triangle with legs 12\tfrac12, area 121212=18\tfrac12\cdot\tfrac12\cdot\tfrac12=\tfrac18. A clean shape, so use a ratio:

P ⁣(x+y12)=1/81=18.P\!\left(x+y\le\tfrac12\right) = \frac{1/8}{1} = \frac{1}{8}.

(b) An average, so use symmetry and linearity of expectation (previewed below). The mirror x1xx \mapsto 1-x sends the uniform point to another uniform point, so E[x]=E[1x]=1E[x]\mathbb{E}[x] = \mathbb{E}[1-x] = 1-\mathbb{E}[x], forcing E[x]=12\mathbb{E}[x] = \tfrac12; the same holds for yy. Adding the two averages,

E[x+y]=E[x]+E[y]=12+12=1.\mathbb{E}[x+y] = \mathbb{E}[x] + \mathbb{E}[y] = \tfrac12+\tfrac12 = 1.

Part (a) wanted the size of a region (ratio); part (b) wanted an average (symmetry plus linearity).

Example
Worked example: three reals and a cube (olympiad flavor)

Real numbers x,y,zx,y,z are chosen independently and uniformly from [0,1][0,1]. Find the probability that they can be the side lengths of the three edges meeting at a corner of a box whose space diagonal is at most 11, i.e. x2+y2+z21x^2+y^2+z^2 \le 1.

Solution. The sample space is the unit cube, volume 11. The favorable region is the part of the cube inside the sphere of radius 11 centered at the origin --- exactly one octant of that ball:

P(x2+y2+z21)=1843π(1)31=π6.P\big(x^2+y^2+z^2\le 1\big) = \frac{\tfrac18\cdot\tfrac{4}{3}\pi(1)^3}{1} = \frac{\pi}{6}.

Three independent uniform reals put us in a cube (volume ratio); the curved boundary is a sphere, so we lean on the known ball-volume formula --- no heavier machinery needed.

Tip

Tip. Count the number of independent random reals: 11\Rightarrow length, 22\Rightarrow area, 33\Rightarrow volume. If the favorable boundary is straight or circular/spherical, reach for a known area/volume formula; if you are asked for an expected value (an average over the region), reach for symmetry and linearity of expectation (Unit 9) before anything fancier.

Inclusion--Exclusion for Probability & the Union Bound

Concept
Probability of a union of many events

For any events A1,,AnA_1,\dots,A_n, the probability that at least one occurs is

P ⁣(i=1nAi)=iP(Ai)i<jP(AiAj)+i<j<kP(AiAjAk)+(1)n+1P ⁣(i=1nAi).P\!\left(\bigcup_{i=1}^{n} A_i\right) = \sum_i P(A_i) - \sum_{i<j} P(A_i\cap A_j) + \sum_{i<j<k} P(A_i\cap A_j\cap A_k) - \cdots + (-1)^{n+1} P\!\left(\bigcap_{i=1}^n A_i\right).

This is inclusion--exclusion applied to probabilities: add the singles, subtract the pairwise overlaps, add back the triples, and so on. When exact overlaps are hard, the union bound (Boole's inequality) gives a quick one-sided estimate by keeping only the first sum:

P ⁣(i=1nAi)i=1nP(Ai).P\!\left(\bigcup_{i=1}^{n} A_i\right) \le \sum_{i=1}^{n} P(A_i).

The union bound is loose but never requires knowing any intersection --- ideal for showing a bad event is unlikely.

Example
Worked example: derangement-style matching (AIME flavor)

Four letters are placed at random into four pre-addressed envelopes, one per envelope. Find the probability that at least one letter lands in its correct envelope.

Solution. Let AiA_i be the event that letter ii is correct. By symmetry P(Ai)=3!4!=14P(A_i)=\tfrac{3!}{4!}=\tfrac14; each pairwise P(AiAj)=2!4!=112P(A_i\cap A_j)=\tfrac{2!}{4!}=\tfrac{1}{12}; each triple 1!4!=124\tfrac{1!}{4!}=\tfrac{1}{24}; the quadruple 124\tfrac{1}{24}. Inclusion--exclusion:

P ⁣(Ai)=(41)14(42)112+(43)124(44)124=112+16124=1524=58.P\!\left(\bigcup A_i\right) = \binom{4}{1}\tfrac14 - \binom{4}{2}\tfrac{1}{12} + \binom{4}{3}\tfrac{1}{24} - \binom{4}{4}\tfrac{1}{24} = 1 - \tfrac12 + \tfrac16 - \tfrac{1}{24} = \frac{15}{24} = \frac{5}{8}.

Equivalently, 11 minus the derangement probability 924=38\tfrac{9}{24}=\tfrac{3}{8}. As nn\to\infty this “at least one fixed point” probability tends to 1e11-e^{-1}.

Example
Worked example: the union bound as a safety net

Each of 1010 independent components fails on a given day with probability 0.0020.002. Bound the probability that the system, which fails if any component fails, fails today.

Solution. Exact inclusion--exclusion is messy, but the union bound is instant:

P(system fails)=P ⁣(i=110Ai)i=110P(Ai)=10(0.002)=0.02.P(\text{system fails}) = P\!\left(\bigcup_{i=1}^{10} A_i\right) \le \sum_{i=1}^{10} P(A_i) = 10(0.002) = 0.02.

The true value 1(0.998)100.01981-(0.998)^{10}\approx 0.0198 is just under the bound --- when the events are rare and barely overlap, the union bound is both quick and tight.

Tip

Tip. For an exact “at least one of many” probability, use full inclusion--exclusion (and exploit symmetry so each kk-fold term is (nk)\binom{n}{k} copies of a single value). For a fast upper bound --- especially to prove something is unlikely --- drop to the union bound P(Ai)P(Ai)P(\bigcup A_i)\le\sum P(A_i). Complementary counting (1P(none)1 - P(\text{none})) is a third route when the AiA_i are independent.

A Catalog of Discrete Distributions & a Preview of Expected Value

Concept
The standard families and their key probabilities

Most contest setups reduce to one of a few named distributions. Recognizing which one you are in gives the probability formula immediately.

  • 3pt
  • Discrete uniform on {1,2,,n}\{1,2,\dots,n\}: each value has probability 1n\tfrac1n; mean n+12\dfrac{n+1}{2}. (One fair die, a random card value.)
  • Binomial B(n,p)B(n,p): number of successes in nn independent trials, each succeeding with probability pp. The chance of exactly kk successes is @@BLOCK0@@
  • Geometric: the trial number of the first success in independent trials with success probability pp. The chance the first success is on trial kk is @@BLOCK1@@

Each is just the equally-likely ratio bookkeeping done once and packaged as a formula.

Example
Worked example: binomial and geometric together

A biased coin shows heads with probability p=13p=\tfrac13. It is flipped repeatedly and independently.

(a) In 55 flips, find P(exactly 2 heads)P(\text{exactly }2\text{ heads}).   (b) Find P(the first head is on flip 4)P(\text{the first head is on flip }4).

Solution. (a) Binomial with n=5,k=2n=5,\,k=2:

P(X=2)=(52)(13)2(23)3=1019827=80243.P(X=2) = \binom{5}{2}\left(\tfrac13\right)^2\left(\tfrac23\right)^3 = 10\cdot\tfrac19\cdot\tfrac{8}{27} = \frac{80}{243}.

(b) Geometric with p=13p=\tfrac13: three failures then a success,

P(X=4)=(23)3(13)=881.P(X=4) = \left(\tfrac23\right)^3\left(\tfrac13\right) = \frac{8}{81}.

Identify the family (fixed number of trials \Rightarrow binomial; wait for the first success \Rightarrow geometric) and the formula writes itself.

Example
Worked example: expected value preview (linking to Unit 9)

A fair die is rolled once. Find the expected value of the number shown. Then find the expected number of heads in 55 flips of the coin with p=13p=\tfrac13.

Solution. Definition. The expected value of a discrete random variable is the probability-weighted average of its values, E[X]=xxP(X=x)\mathbb{E}[X]=\sum_x x\,P(X=x). For the die (discrete uniform on 11--66):

E[X]=1+2+3+4+5+66=72.\mathbb{E}[X] = \frac{1+2+3+4+5+6}{6} = \frac{7}{2}.

For the coin, rather than sum the binomial terms, use linearity of expectation: each flip contributes an expected p=13p=\tfrac13 head, so

E[heads]=513=53.\mathbb{E}[\text{heads}] = 5\cdot\tfrac13 = \frac{5}{3}.

In general E[B(n,p)]=np\mathbb{E}[B(n,p)]=np and E[geometric(p)]=1p\mathbb{E}[\text{geometric}(p)]=\tfrac1p. Unit 9 develops linearity of expectation into a power tool; for now, note it lets you average without ever computing the full distribution.

Tip

Tip. Classify before you compute: fixed number of independent trials, count successes \Rightarrow binomial (nk)pk(1p)nk\binom{n}{k}p^k(1-p)^{n-k}; repeat until the first success \Rightarrow geometric (1p)k1p(1-p)^{k-1}p; one draw from equally likely values \Rightarrow uniform. For averages, prefer E[X]=xP(X=x)\mathbb{E}[X]=\sum x\,P(X=x), and remember E[B(n,p)]=np\mathbb{E}[B(n,p)]=np and E[geometric]=1p\mathbb{E}[\text{geometric}]=\tfrac1p as instant shortcuts (full treatment in Unit 9).

Formulas, Proofs & Tips

Tip
Probability rules
P(A)=favourabletotal,P(not A)=1P(A),P(AB)=P(A)+P(B)P(AB)P(A)=\frac{\text{favourable}}{\text{total}},\quad P(\text{not }A)=1-P(A),\quad P(A\cup B)=P(A)+P(B)-P(A\cap B)

What it means. Probability is a fraction of equally likely outcomes; the union rule avoids double counting.

Example. One fair die: P(even)=36=12P(\text{even})=\tfrac36=\tfrac12.

Why it works. Adding P(A)P(A) and P(B)P(B) counts every outcome in both events twice, so the overlap is subtracted once. The complement rule follows because AA and "not AA" together cover everything, totalling 11.

Tip. When a question says "at least one", the complement is usually far quicker: 1P(none)1-P(\text{none}).