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🎯⭐ INTERACTIVE LESSON

Alternating Series

Learn step-by-step with interactive practice!

Alternating Series - Complete Interactive Lesson

Part 1: Core Concepts

Alternating Series — The Alternating Series Test

Part 1 of 7 — Foundations

What Is an Alternating Series?

A series whose terms alternate in sign:

∑n=1∞(−1)n+1bn=b1−b2+b3−b4+⋯\sum_{n=1}^\infty (-1)^{n+1} b_n = b_1 - b_2 + b_3 - b_4 + \cdots

or equivalently ∑(−1)nbn\sum (-1)^n b_n where bn>0b_n > 0.

The Alternating Series Test (AST)

If bn>0, bn+1≤bn (decreasing), and lim⁡n→∞bn=0, then ∑(−1)n+1bn converges.\boxed{\text{If } b_n > 0,\ b_{n+1} \le b_n \text{ (decreasing), and } \lim_{n\to\infty} b_n = 0, \text{ then } \sum (-1)^{n+1} b_n \text{ converges.}}

The Three Hypotheses

#ConditionWhy It's Needed
1bn>0b_n > 0Terms truly alternate
2bnb_n eventually decreasingPartial sums "squeeze" toward limit
3lim⁡bn=0\lim b_n = 0Without this, Divergence Test kicks in

AP Tip: On the AP exam, you must explicitly verify ALL three conditions. Simply stating "by AST" is not sufficient for full credit.

Classic Examples

Alternating Harmonic Series:

∑n=1∞(−1)n+1n=1−12+13−14+⋯=ln⁡2\sum_{n=1}^\infty \frac{(-1)^{n+1}}{n} = 1 - \frac{1}{2} + \frac{1}{3} - \frac{1}{4} + \cdots = \ln 2

Check: bn=1/n>0b_n = 1/n > 0 ✓, 1/(n+1)<1/n1/(n+1) < 1/n ✓, 1/n→01/n \to 0 ✓ → Converges by AST.

Non-Example:

∑n=1∞(−1)n+1nn+1\sum_{n=1}^\infty \frac{(-1)^{n+1} n}{n+1}

bn=n/(n+1)→1≠0b_n = n/(n+1) \to 1 \neq 0. The third condition FAILS. This series diverges by the Divergence Test.

Why "Eventually Decreasing" Suffices

bnb_n only needs to be decreasing for n≥Nn \ge N (some fixed NN). A finite number of "bad" terms don't affect convergence.

To show decreasing: verify bn+1<bnb_{n+1} < b_n, or equivalently bn+1/bn<1b_{n+1}/b_n < 1, or show f′(x)<0f'(x) < 0 for the continuous version.

Practice: Applying the AST

Verify AST Conditions

Quick Check

Summary

  • Alternating Series Test: three conditions (bn>0b_n > 0, decreasing, →0\to 0)
  • Must verify ALL three explicitly on the AP exam
  • "Eventually decreasing" is sufficient
  • The AST tells you a series converges but does NOT give the sum

Next: Part 2 — Alternating Series Error Bound (Remainder Estimation).

Part 2: Worked Examples

Alternating Series — Error Bound

Part 2 of 7 — The Alternating Series Remainder

The Error Bound Theorem

If ∑(−1)n+1bn\sum (-1)^{n+1} b_n satisfies the AST conditions and SS is the exact sum, then the error after NN terms satisfies:

∣S−SN∣≤bN+1\boxed{|S - S_N| \le b_{N+1}}

The error is bounded by the absolute value of the first omitted term.

Why This Works

The partial sums of an alternating series "bracket" the true sum:

S1>S>S2,S3>S>S4,…S_1 > S > S_2,\quad S_3 > S > S_4,\quad \ldots

Each new term overshoots and then corrects, so the error is at most the magnitude of the next term.

Example

∑n=1∞(−1)n+1n3\sum_{n=1}^\infty \frac{(-1)^{n+1}}{n^3}. Approximate SS using 4 terms.

S4=1−18+127−164=1728−216+64−271728≈0.8958S_4 = 1 - \frac{1}{8} + \frac{1}{27} - \frac{1}{64} = \frac{1728 - 216 + 64 - 27}{1728} \approx 0.8958

Error ≤b5=1/125=0.008\le b_5 = 1/125 = 0.008. So S≈0.896±0.008S \approx 0.896 \pm 0.008.

AP Tip: This error bound appears almost every year on the BC exam, often in FRQ. Know it cold.

Finding NN for a Given Accuracy

Problem: How many terms of ∑(−1)n+1/n2\sum (-1)^{n+1}/n^2 ensure error <0.01< 0.01?

Solution: Need bN+1<0.01b_{N+1} < 0.01, i.e., 1(N+1)2<0.01\frac{1}{(N+1)^2} < 0.01.

(N+1)2>100  ⟹  N+1>10  ⟹  N≥10(N+1)^2 > 100 \implies N+1 > 10 \implies N \ge 10

So 10 terms give accuracy within 0.010.01.

Important Distinctions

FeatureAlternating Series ErrorLagrange Error (Taylor)
Formula$R_N
Applies toAny alternating series meeting ASTTaylor polynomial remainders
Easy to use?Very easyRequires finding MM
On AP examVery commonAlso very common

Error Bound Practice

Error Analysis

Finding Number of Terms

Summary

  • Error ≤∣bN+1∣\le |b_{N+1}| = first omitted term
  • To find NN for accuracy ϵ\epsilon: solve bN+1<ϵb_{N+1} < \epsilon
  • Partial sums alternately overestimate and underestimate
  • This is one of the MOST tested concepts on the BC exam

Next: Part 3 — Absolute vs. Conditional Convergence in Alternating Series.

Part 3: Problem-Solving Patterns

Alternating Series — Absolute vs. Conditional Convergence

Part 3 of 7 — Classification of Alternating Series

Review of Definitions

ClassificationMeaning
Absolutely convergent$\sum
Conditionally convergent∑an\sum a_n converges but $\sum

Classification Procedure for Alternating Series

Step 1: Compute ∑∣an∣=∑bn\sum |a_n| = \sum b_n (remove the (−1)n(-1)^n factor).

Step 2: If ∑bn\sum b_n converges → absolutely convergent (done).

Step 3: If ∑bn\sum b_n diverges → check AST conditions on original series.

  • If AST conditions met → conditionally convergent
  • If AST fails → divergent

The Four Essential Examples

| Series | ∑∣an∣\sum |a_n| | ∑an\sum a_n | Classification | |--------|-------------|-----------|---------------| | ∑(−1)n/n2\sum (-1)^n/n^2 | ∑1/n2\sum 1/n^2 conv. (p=2p=2) | Converges | Absolute | | ∑(−1)n/n\sum (-1)^n/n | ∑1/n\sum 1/n div. | AST works → conv. | Conditional | | ∑(−1)n/n\sum (-1)^n/\sqrt{n} | ∑1/n\sum 1/\sqrt{n} div. (p=1/2p=1/2) | AST works → conv. | Conditional | | ∑(−1)n⋅n/(n+1)\sum (-1)^n \cdot n/(n+1) | Diverges | an↛0a_n \not\to 0 → div. | Divergent |

Connection to Interval of Convergence

This classification matters most at endpoints of intervals of convergence for power series.

Example: ∑n=1∞xnn\sum_{n=1}^\infty \frac{x^n}{n}

Ratio test: ∣x∣<1|x| < 1 → converges. At the endpoints:

  • x=1x = 1: ∑1/n\sum 1/n diverges
  • x=−1x = -1: ∑(−1)n/n\sum (-1)^n/n converges (conditionally)

So the interval of convergence is [−1,1)[-1, 1).

At endpoints, alternating series often converge conditionally while the positive version diverges.\boxed{\text{At endpoints, alternating series often converge conditionally while the positive version diverges.}}

AP Tip: When finding intervals of convergence, ALWAYS test endpoints separately. Expect at least one endpoint to involve an alternating series.

Classification Practice

Endpoint Classification

Classification Challenge

Summary

  • Test ∑∣an∣\sum |a_n| first; if it converges, you have absolute convergence
  • If ∑∣an∣\sum |a_n| diverges but ∑an\sum a_n converges (via AST), it's conditional
  • Endpoint testing for power series frequently involves this classification
  • ∑(−1)n/np\sum (-1)^n/n^p: absolute if p>1p > 1, conditional if 0<p≤10 < p \le 1

Next: Part 4 — Alternating Series and Taylor Polynomials.

Part 4: Graphs and Interpretation

Alternating Series — Connections to Taylor Series

Part 4 of 7 — Alternating Series in Taylor/Maclaurin Context

Key Maclaurin Series That Alternate

FunctionSeriesNotes
e−xe^{-x}∑n=0∞(−x)nn!=∑(−1)nxnn!\sum_{n=0}^\infty \frac{(-x)^n}{n!} = \sum \frac{(-1)^n x^n}{n!}Alternates for x>0x > 0
sin⁡x\sin x∑n=0∞(−1)nx2n+1(2n+1)!\sum_{n=0}^\infty \frac{(-1)^n x^{2n+1}}{(2n+1)!}Alternates for all x>0x > 0
cos⁡x\cos x∑n=0∞(−1)nx2n(2n)!\sum_{n=0}^\infty \frac{(-1)^n x^{2n}}{(2n)!}Alternates for all x>0x > 0
ln⁡(1+x)\ln(1+x)∑n=1∞(−1)n+1xnn\sum_{n=1}^\infty \frac{(-1)^{n+1} x^n}{n}Alternates for 0<x≤10 < x \le 1
arctan⁡x\arctan x∑n=0∞(−1)nx2n+12n+1\sum_{n=0}^\infty \frac{(-1)^n x^{2n+1}}{2n+1}Alternates for 0<x≤10 < x \le 1

When a Taylor series alternates, you can use the alternating series error bound for the remainder.\boxed{\text{When a Taylor series alternates, you can use the alternating series error bound for the remainder.}}

AP Tip: The alternating series error bound is often EASIER to apply than the Lagrange error bound. Use it whenever the series alternates.

Example: Estimating cos⁡(0.5)\cos(0.5)

cos⁡x=1−x22!+x44!−x66!+⋯\cos x = 1 - \frac{x^2}{2!} + \frac{x^4}{4!} - \frac{x^6}{6!} + \cdots

Using 3 terms (n=0,1,2n = 0, 1, 2):

P4(0.5)=1−0.252+0.062524=1−0.125+0.002604=0.877604P_4(0.5) = 1 - \frac{0.25}{2} + \frac{0.0625}{24} = 1 - 0.125 + 0.002604 = 0.877604

Error ≤∣\le |first omitted term∣=(0.5)6720=1/64720≈0.0000217| = \frac{(0.5)^6}{720} = \frac{1/64}{720} \approx 0.0000217

Compare: cos⁡(0.5)=0.877583…\cos(0.5) = 0.877583\ldots, so actual error ≈0.000021\approx 0.000021 ✓

Alternating vs. Lagrange Error Bound

For alternating Taylor series, both bounds work:

  • Alternating: ∣R∣≤∣next term∣|R| \le |\text{next term}| — easy!
  • Lagrange: ∣Rn(x)∣≤M∣x−c∣n+1(n+1)!|R_n(x)| \le \frac{M|x-c|^{n+1}}{(n+1)!} — need to find MM

The alternating bound is usually tighter and easier. Use it when available!

Taylor + AST Practice

Error Bound Application

Error Computation

Summary

  • Many important Taylor series alternate for certain xx values
  • When alternating, use AST error bound: easier than Lagrange
  • Key functions: sin⁡x\sin x, cos⁡x\cos x, e−xe^{-x}, ln⁡(1+x)\ln(1+x), arctan⁡x\arctan x
  • On the AP exam, choose the simpler error bound when both apply

Next: Part 5 — AP Exam Strategies for Alternating Series.

Part 5: Applications

Alternating Series — AP Exam Strategies

Part 5 of 7 — FRQ & MC Techniques

Common AP Question Types

TypeWhat They AskKey Steps
AST verification"Show the series converges"State and verify all 3 conditions
Error bound"Approximate with error < ε"Find NN where bN+1<ϵb_{N+1} < \epsilon
Classification"Absolutely, conditionally, or diverges?"Test $\sum
Endpoint analysis"Find interval of convergence"Test each endpoint separately
Taylor connection"Use AST error bound for Pn(x)P_n(x)"Identify alternating structure

FRQ Template: AST Verification

When the AP exam says "show the series converges using the Alternating Series Test":

  1. Identify: "This is an alternating series with bn=…b_n = \ldots"
  2. Positive: "bn>0b_n > 0 for all n≥1n \ge 1" ✓
  3. Decreasing: "bn+1≤bnb_{n+1} \le b_n because f′(x)=…<0f'(x) = \ldots < 0" ✓
  4. Limit: "lim⁡n→∞bn=…=0\lim_{n\to\infty} b_n = \ldots = 0" ✓
  5. Conclude: "Therefore, ∑(−1)n+1bn\sum (-1)^{n+1} b_n converges by the AST." ✓

AP Tip: Omitting any of the three verifications costs points. Even if one seems "obvious," state it explicitly.

Common AP Mistakes to Avoid

Mistake 1: Forgetting to check bn→0b_n \to 0

∑(−1)nnn+1\sum (-1)^n \frac{n}{n+1}: Students assume AST applies because it alternates. But bn=n/(n+1)→1≠0b_n = n/(n+1) \to 1 \neq 0. Diverges!

Mistake 2: Using ana_n instead of bnb_n

bnb_n is the POSITIVE part. Don't check if (−1)nbn→0(-1)^n b_n \to 0 — check if bn→0b_n \to 0.

Mistake 3: Not showing "decreasing"

Must show bn+1<bnb_{n+1} < b_n or use f′(x)<0f'(x) < 0. Don't just assert it.

Mistake 4: Confusing AST error bound with Lagrange

Alternating ErrorLagrange Error
$R
No MM neededMust bound f(n+1)f^{(n+1)}
Only for alternating seriesFor any Taylor remainder

AP-Style Problems

Exam Strategy Decisions

AP FRQ Practice

Exam Strategy Summary

  • Always verify ALL three AST conditions explicitly
  • Error bound: ∣S−SN∣≤bN+1|S - S_N| \le b_{N+1}
  • Overestimate vs. underestimate: depends on parity of NN and sign of first term
  • Odd NN + positive first term → overestimate
  • Even NN + positive first term → underestimate

Next: Part 6 — Problem-Solving Workshop.

Part 6: Exam Strategy

Alternating Series — Problem-Solving Workshop

Part 6 of 7 — Mixed Practice

Work through these problems systematically. For each, identify whether to apply AST, error bound, or classification.

Warm-Up Review

ConceptFormula/Rule
AST conditionsbn>0b_n > 0, decreasing, →0\to 0
Error bound$
Absolute conv.$\sum
Conditional conv.∑an\sum a_n conv., $\sum

Workshop Problems

Error Bound Workshop

Computation Challenge

Workshop Takeaways

  • Check AST conditions systematically
  • Error bound problems: solve bN+1<ϵb_{N+1} < \epsilon
  • Classification: test ∑∣an∣\sum |a_n| first
  • Factorial denominators converge fast, harmonic-type converge slowly

Next: Part 7 — Comprehensive Review.

Part 7: Mixed Review

Alternating Series — Comprehensive Review

Part 7 of 7 — Full Topic Review

Complete Reference

ConceptKey Formula/Rule
ASTbn>0b_n > 0, bn+1≤bnb_{n+1} \le b_n, bn→0b_n \to 0 → converges
Error Bound$
Over/UnderOdd NN + positive first → over; Even NN → under
Absolute$\sum
Conditional∑an\sum a_n conv. but $\sum
RearrangementConditionally conv. → rearrange to any sum

Alternating series: check conditions, bound the error, classify the convergence.\boxed{\text{Alternating series: check conditions, bound the error, classify the convergence.}}

Comprehensive Review MC

Final Classification Drill

Final Error Bound Problem

Alternating Series — Complete Summary

You've mastered:

  • Alternating Series Test — the three conditions and verification
  • Error Bound — first omitted term bounds the error
  • Over/Underestimate — parity of partial sum count
  • Absolute vs. Conditional — classification procedure
  • Taylor Series Connection — AST error bound as an alternative to Lagrange
  • AP Exam Strategies — full justification requirements

Key Fact: Alternating series and error bounds appear on virtually every BC exam. This is one of the highest-yield topics for your score.

Up Next: Power Series — representation, convergence, and manipulation.