Statistics Tool · 2025

NormalCDF Calculator

Free normalcdf calculator — enter your mean, standard deviation, lower bound, and upper bound to instantly get the normal CDF probability. Includes Z-score conversion, area visualization, standard normal Z-table, step-by-step examples, and complete statistics guide.

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NormalCDF Calculator
normalcdf(lower, upper, μ, σ) → probability instantly
Distribution Parameters
Bounds

Probability P(lower ≤ X ≤ upper)
as decimal (0–1)
Percentage
× 100
Lower Z-Score
(lower − μ) ÷ σ
Upper Z-Score
(upper − μ) ÷ σ

What Is normalcdf?

normalcdf stands for normal cumulative distribution function. It calculates the probability that a normally distributed random variable falls between a lower and upper bound. The result is a number between 0 and 1 — multiply by 100 for a percentage.

The term "normalcdf" is most commonly associated with the TI-84 graphing calculator, where it appears as a built-in function: normalcdf(lower, upper, μ, σ). This online calculator does the same calculation instantly without requiring a physical calculator.

normalcdf in Plain English

"What percentage of values in my data fall between X₁ and X₂, assuming a normal (bell-curve) distribution with a given mean and standard deviation?"

Input: lower bound, upper bound, mean (μ), standard deviation (σ)
Output: probability (decimal 0–1) = area under the normal curve between the bounds

When Is normalcdf Used?

  • Statistics class — homework problems requiring normal probability calculations
  • AP Statistics exam — finding probabilities from normal distributions (MCQ and FRQ)
  • Quality control — finding what percentage of products meet spec tolerances
  • Finance — calculating probabilities in option pricing models (Black-Scholes)
  • Standardized tests — converting SAT/ACT percentiles from raw scores

Z-Score Conversion Formula

To use a standard normal table (Z-table), you first convert your raw value X to a Z-score. The Z-score tells you how many standard deviations away from the mean your value is.

Z-Score Formula
Z = (X − μ) ÷ σ
X = your raw value · μ = population mean · σ = standard deviation
Z-ScoreMeaningP(X ≤ value) approx.
Z = −3.03 SD below mean0.0013 (0.13%)
Z = −2.02 SD below mean0.0228 (2.28%)
Z = −1.01 SD below mean0.1587 (15.87%)
Z = 0.0At the mean0.5000 (50.00%)
Z = +1.01 SD above mean0.8413 (84.13%)
Z = +1.64590th percentile0.9500 (95.00%)
Z = +1.9695th percentile0.9750 (97.50%)
Z = +2.02 SD above mean0.9772 (97.72%)
Z = +3.03 SD above mean0.9987 (99.87%)

3 Worked Examples — NormalCDF Step by Step

Example 1 — Test Scores (Between Two Values)

Problem: Test scores are normally distributed with μ = 75, σ = 10. What percentage of students scored between 65 and 90?

1
Lower Z: (65 − 75) ÷ 10 = −1.0
2
Upper Z: (90 − 75) ÷ 10 = +1.5
3
P(−1.0 ≤ Z ≤ 1.5) = Φ(1.5) − Φ(−1.0) = 0.9332 − 0.1587 = 0.7745 = 77.45%

Example 2 — Below a Value (Left Tail)

Problem: Heights are normally distributed with μ = 68 inches, σ = 3 inches. What percentage of people are shorter than 65 inches?

1
Lower bound = −∞ (no lower limit). Upper = 65.
2
Z: (65 − 68) ÷ 3 = −1.0
3
P(X < 65) = Φ(−1.0) = 0.1587 = 15.87%

Example 3 — Above a Value (Right Tail)

Problem: IQ scores have μ = 100, σ = 15. What percentage score above 130?

1
Lower = 130. Upper = +∞ (right tail)
2
Z: (130 − 100) ÷ 15 = +2.0
3
P(X > 130) = 1 − Φ(2.0) = 1 − 0.9772 = 0.0228 = 2.28%

Using normalcdf on a TI-84 Calculator

The TI-84 family of calculators (TI-84 Plus, TI-84 Plus CE, TI-83 Plus) all have normalcdf as a built-in function.

Steps to Use normalcdf on TI-84

  1. Press 2ND then VARS to open the DISTR menu
  2. Select normalcdf( — option 2 in the DISTR menu
  3. Enter: normalcdf(lower, upper, μ, σ)
  4. Press ENTER to calculate
TI-84 Infinity Shortcuts

For left-tail calculations (no lower bound): use −1E99 as the lower bound (type: negative, 1, 2nd, comma for E, 99). For right-tail calculations (no upper bound): use 1E99 as the upper bound. Example: P(X ≤ 1.5) → normalcdf(−1E99, 1.5, 0, 1) = 0.9332

Probability TypeTI-84 InputExample
P(X ≤ b) — left tailnormalcdf(−1E99, b, μ, σ)normalcdf(−1E99, 75, 70, 10) = 0.6915
P(X ≥ a) — right tailnormalcdf(a, 1E99, μ, σ)normalcdf(85, 1E99, 70, 10) = 0.0668
P(a ≤ X ≤ b) — betweennormalcdf(a, b, μ, σ)normalcdf(60, 80, 70, 10) = 0.6827
Standard normalnormalcdf(a, b, 0, 1)normalcdf(−1, 1, 0, 1) = 0.6827

Standard Normal Z-Table (Selected Values)

The Z-table gives the cumulative probability P(Z ≤ z) for the standard normal distribution (μ=0, σ=1). Use this for manual normalcdf calculations or to verify your calculator results.

Z.00.01.02.03.04.05.06.07.08.09

The 68-95-99.7 Rule (Empirical Rule)

The empirical rule describes what percentage of data falls within 1, 2, and 3 standard deviations of the mean in a normal distribution. Memorizing this rule is essential for AP Statistics.

RangeZ-Score RangeProbabilitynormalcdf Verification
μ ± 1σ−1.0 to +1.068.27%normalcdf(−1, 1, 0, 1) = 0.6827
μ ± 2σ−2.0 to +2.095.45%normalcdf(−2, 2, 0, 1) = 0.9545
μ ± 3σ−3.0 to +3.099.73%normalcdf(−3, 3, 0, 1) = 0.9973

NormalCDF Calculator — FAQ

What does normalcdf calculate? +
normalcdf calculates the cumulative distribution function (CDF) of a normal distribution — the probability that a normally distributed random variable falls between two values (lower and upper bounds). The result is between 0 and 1. Multiply by 100 for percentage. On a TI-84: normalcdf(lower, upper, μ, σ). This online calculator does the same calculation.
What is the difference between normalcdf and normpdf? +
normalcdf gives the probability (area) between two bounds — the most useful function for answering "what percentage of values fall between X and Y?" normpdf (normal probability density function) gives the height of the bell curve at a specific point — used for graphing the distribution, not for probability calculations. For almost all statistics homework and exam problems, normalcdf is what you need.
How do I use normalcdf for a left-tail probability? +
For P(X ≤ b) — the probability of being below a value — use negative infinity as your lower bound. In this calculator, leave the lower bound field blank or enter a very large negative number. On a TI-84, enter −1E99 as the lower bound: normalcdf(−1E99, b, μ, σ). Example: P(X ≤ 85) with μ=80, σ=10: normalcdf(−1E99, 85, 80, 10) = 0.6915.
How do I use normalcdf for a right-tail probability? +
For P(X ≥ a) — the probability of being above a value — use positive infinity as your upper bound. Leave the upper bound blank or enter a very large positive number. On TI-84: normalcdf(a, 1E99, μ, σ). Alternatively, calculate P(X ≤ a) with the left-tail method and subtract from 1: P(X ≥ a) = 1 − P(X ≤ a).
What are the normalcdf inputs? +
normalcdf takes 4 inputs: (1) Lower bound — the minimum value of your range (use −1E99 for no lower bound). (2) Upper bound — the maximum value (use 1E99 for no upper bound). (3) μ (mu) — the mean of your normal distribution. (4) σ (sigma) — the standard deviation. For the standard normal distribution, use μ=0 and σ=1 and enter Z-scores directly as your bounds.
What is a Z-score? +
A Z-score measures how many standard deviations a value is from the mean: Z = (X − μ) ÷ σ. A Z-score of 0 means the value equals the mean. Z = +1 means 1 standard deviation above mean. Z = −2 means 2 standard deviations below mean. Converting to Z-scores allows you to use the standard normal table (μ=0, σ=1) for any normal distribution.
How accurate is this normalcdf calculator? +
This calculator uses a precise numerical approximation of the complementary error function (erfc) to compute the normal CDF. Results match TI-84 and scientific computing outputs to 6+ decimal places for most inputs. For extreme Z-scores (|Z| > 8), the result approaches 0 or 1 so precisely that floating-point representation makes exact comparison difficult, but the displayed result is accurate for all practical statistics applications.
What is the 68-95-99.7 rule? +
The 68-95-99.7 rule (empirical rule) states that in a normal distribution: 68.27% of values fall within 1 standard deviation of the mean (μ ± 1σ), 95.45% fall within 2 standard deviations (μ ± 2σ), and 99.73% fall within 3 standard deviations (μ ± 3σ). You can verify these with normalcdf: normalcdf(−1, 1, 0, 1) = 0.6827, normalcdf(−2, 2, 0, 1) = 0.9545, normalcdf(−3, 3, 0, 1) = 0.9973.