foundation25 min studyLesson 118 in Mathematics

Permutations and combinations: decide whether order survives

Derive factorial formulas from reversible constructions and explain binomial identities by subsets.

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What you’ll understand

  • Count ordered and unordered selections without confusing them.
  • Justify division by a constant number of encodings.
  • Prove Pascal's identity and the binomial theorem by classifying choices.

Choosing a chair and a secretary differs from choosing a two-person committee. The same two people produce two role assignments but only one committee. Factorials and binomial coefficients record this distinction. Rather than memorizing which formula goes with a word problem, construct the object, ask how many construction histories describe it, and divide only when that number is the same for every object being counted.

Order creates a sequence of shrinking choices

To arrange n distinct objects, choose the first in n ways, the next in n minus one, and continue until one remains. This gives n factorial. An ordered selection of k distinct objects stops after k choices and has n factorial divided by n minus k factorial possibilities. The condition zero at most k at most n belongs to the formula. Selecting nothing has one outcome, the empty sequence, which explains the convention zero factorial equals one.

If objects repeat, the construction histories may no longer be distinct outcomes. Arranging the letters in LEVEL first treats the two Ls and two Es as separately labelled, then forgets those labels. Every visible arrangement has two factorial times two factorial labelled descriptions, so dividing gives thirty. Uniform multiplicity justifies the division. If different outputs have different numbers of descriptions, dividing by a single convenient constant cannot correct the count.

P(n,k)=n!(n−k)!,(nk)=P(n,k)k!=n!k!(n−k)!,0≤k≤nP(n,k)=\frac{n!}{(n-k)!},\qquad \binom nk=\frac{P(n,k)}{k!}=\frac{n!}{k!(n-k)!},\qquad 0\le k\le n
The first count retains order. A k-element subset has exactly k! distinct orderings, so the second count forgets order uniformly.↔ Scroll wide equations horizontally.

A subset is an unordered selection

The binomial coefficient counts k-element subsets of an n-element labelled set. Choosing the subset is equivalent to choosing its complement, so choosing k and choosing n minus k have equal counts. This symmetry is visible in the factorial formula, but the complement bijection explains it directly. Labels matter: choosing two positions in a word is not the same as choosing two distinct letter values when letters repeat. Specify the underlying set before inserting numbers.

Pascal's identity classifies subsets according to a distinguished object. Those omitting it choose all k members from the remaining n objects. Those containing it choose their other k minus one members from those n objects. The two classes are disjoint and cover every k-element subset of an n plus one element set. This argument also supplies the recurrence for constructing a row of binomial coefficients without evaluating large factorials.

(n+1k)=(nk)+(nk−1),(u+v)n=∑k=0n(nk)un−kvk\binom{n+1}{k}=\binom nk+\binom n{k-1},\qquad (u+v)^n=\sum_{k=0}^{n}\binom nk u^{n-k}v^k
Pascal's identity counts subsets by whether they contain a fixed member. The binomial expansion counts which k of n factors contribute v, with commuting scalars u and v.↔ Scroll wide equations horizontally.

Expansion coefficients count choices of positions

Expand a product of n copies of u plus v by choosing one term from each factor. A term with k choices of v and n minus k choices of u has the same monomial regardless of which positions supplied v, because scalar multiplication commutes. The number of position subsets is the binomial coefficient. For noncommuting matrices, different position choices need not give the same product, so this scalar formula cannot be transferred without additional assumptions.

At u and v both equal one, the expansion counts every subset and gives two to the nth power. At u equal one and v equal minus one, the alternating sum is zero for positive n. A combinatorial explanation pairs each subset with the subset obtained by toggling one fixed element; the pairing changes parity. Such interpretations let an algebraic identity reveal a reversible operation on objects instead of remaining an unexplained cancellation of symbols.

Roles versus membership

From six people, choose a chair, a secretary, and a treasurer, then compare with choosing an unlabelled three-person committee.

  1. For the role assignment, choose a chair in six ways, a different secretary in five, and a third person as treasurer in four. There are 6 × 5 × 4 = 120 ordered outcomes.

  2. Each three-person committee appears in exactly 3! = 6 role assignments because its three distinct members can fill the three distinct roles in all six orders.

  3. Divide 120 by 6 to obtain 20 committees. Check by complement symmetry: choosing three members to include is equivalent to choosing three of six to leave out.

The result

There are 120 role assignments and 20 committees. The factor of six has a precise meaning: equal representation multiplicity.

Explore the model

Interactive lab

Interactive lab · probability

Predict binomial bar coefficients

Set success probability p=1/2, then vary trials n. Predict that the bar at k successes is (nk)/2n\binom nk/2^n because each binary history has equal weight. Compare symmetric bars and the central concentration. Changing p retains the subset coefficient but changes the weight to pk(1−p)n−kp^k(1-p)^{n-k}. The lab displays exact probabilities, not sampled frequencies or lists of permutations.

Predict binomial bar coefficientsThese are exact binomial probabilities for independent trials with one fixed success probability. Playback tours the distribution; it does not resample outcomes. Current readings: Mean · np: 5; Variance · np(1−p): 2.5; Total probability: 1. All parameters are adjustable with the labeled controls below.03581000.070.150.220.3Successes kP(X = k)k = 0 · P = 9.77e-4
Exact probabilityHighlighted outcomeMean np
Mean · np
5
Variance · np(1−p)
2.5
Total probability
1
t = 0 s

These are exact binomial probabilities for independent trials with one fixed success probability. Playback tours the distribution; it does not resample outcomes. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.

Look forThe binomial coefficient counts success positions; a probability model weights each history.

Check your understanding

Why may an ordered k-person selection be divided by k! to count k-person committees?

Try it yourself

Count five-bit strings with exactly two ones, then count those with at least one one. Explain both answers using subsets of positions.

Take a moment to make a prediction or work it out on paper. Open a hint when you need one.

Give me a hint

For the second question, count all strings and remove the unique all-zero string.

Compare with the solution

A string with exactly two ones is specified by a two-element subset of five positions, giving C(5,2) = 10 strings. Every five-bit string corresponds to one subset of positions, so there are 2 to the fifth power = 32 strings in total. Removing the empty subset, which gives the all-zero string, leaves 31 strings with at least one one. The two questions impose different restrictions and must not share a single binomial coefficient.

Take it with you

  • Ordered roles and unordered membership are different objects.
  • Division is justified by the same number of encodings for every output.
  • Binomial coefficients count subsets of positions and explain expansion coefficients.

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