P-value from Z-score Calculator
Already have a Z statistic and need a p-value for the write-up? Enter any real-valued Z; the tool uses the standard normal CDF Φ(z) to return the left-tail, right-tail and two-sided p-values, plus a verdict against α = 0.05 / 0.01 / 0.001 — the closing move of a t-test, proportion test, regression coefficient or A/B-test write-up.
Enter a finite Z-score.
Two-sided p (2·min(p_left, p_right))
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Left-tail p (P(Z ≤ z))
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Right-tail p (P(Z ≥ z))
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Standard normal CDF Φ(z)
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Uses the Abramowitz & Stegun 7.1.26 erf approximation (error ≈ 1.5e-7); matches Excel, SciPy and R's pnorm() to six decimals.
Formula
Φ(z) = ½ · [1 + erf(z / √2)] p_left = Φ(z) p_right = 1 − Φ(z) p_two = 2 · min(p_left, p_right)
- · One-tailed vs two-tailed: pre-specify the alternative hypothesis. Test for "any change" → two-tailed. Directional ("greater than baseline" or "less than baseline") → one-tailed. Decide before seeing the data, not after.
- · Reference critical Z values: 1.645 → one-tailed p = 0.05; 1.96 → two-tailed p = 0.05; 2.576 → two-tailed p = 0.01; 3.291 → two-tailed p = 0.001.
- · The Z distribution assumes large samples (n ≥ 30) or a known population variance. With small n and unknown σ, use Student's t (df = n − 1) — Z will overstate significance.
- · Internally uses the Abramowitz & Stegun 7.1.26 erf approximation (max error ≈ 1.5 × 10⁻⁷). Outputs match Excel NORM.S.DIST(), Python scipy.stats.norm.cdf() and R pnorm() to six decimals.
- · p < 0.05 does NOT mean "the hypothesis is true" — it means "if the null were true, the probability of seeing this result or something more extreme is < 5%." The American Statistical Association 2016 statement urges reporting an effect size (Cohen's d, odds ratio, etc.) alongside p, not p alone.
- · References: Abramowitz & Stegun, *Handbook of Mathematical Functions* §7.1.26; Wasserstein & Lazar, "ASA Statement on p-values" (2016).
Frequently asked
My Z is 1.96 — why is the two-sided p = 0.05 and not 0.025?
Because "two-sided" sums both tails. At z = 1.96 the right-tail P(Z ≥ 1.96) ≈ 0.025, but the two-sided test asks for P(|Z| ≥ 1.96) — the probability of landing above +1.96 OR below −1.96: 0.025 + 0.025 = 0.05. The two-sided form is the default in most reporting because researchers usually do not pre-specify a direction. If the study protocol commits to a directional hypothesis up-front (e.g. "the new drug must outperform placebo"), one-sided is legitimate — it halves the p-value for the same z. But switching from two-sided to one-sided *after* seeing the data to chase significance is p-hacking and grounds for rejection by most medical journals.
When should I use Z, and when should I use t?
Same shape (symmetric, bell-curved); they differ in the tails. Use Z when the population standard deviation σ is known, or when n is large enough (≥ 30) for the sample s to closely approximate σ — then Z's tails are accurate. Use t when σ is unknown and n is small: t accounts for the extra uncertainty in s and has slightly heavier tails, with df = n − 1. Practical rules: (a) clinical trials and A/B tests usually have n > 30 → Z. (b) Psychology / social-science experiments often have n = 10–25 → t. (c) Proportion z-tests are not n-limited (σ comes from p(1−p)), but the large-sample condition n·p > 5 and n·(1−p) > 5 still applies. (d) Regression coefficients are t-tested with df = n − k − 1, not z. Borderline results (p ≈ 0.05) shift by ~0.005 between the two distributions at n = 20: t pushes up, Z pushes down.
Why does p < 0.05 NOT mean "my hypothesis is true"?
The strict definition is "the probability of observing data this extreme, given that the null hypothesis H₀ is true" — a conditional probability P(data | H₀), not P(H₀ | data) or P(H₁ | data). So p = 0.03 does NOT mean "97 % chance the hypothesis is true" or "97 % chance the next replication will be significant." It simply says: "if H₀ were true, this kind of data would arise 3 % of the time" — a Type-I error is still possible. Second, p is highly sample-size dependent: with large n almost any tiny, meaningless effect will be "significant"; with small n a real, large effect may not be. The 2016 ASA statement explicitly recommends reporting effect size (Cohen's d, relative risk, 95% CI) alongside the p-value, never the p-value alone. A growing movement (Amrhein, Greenland & McShane, *Nature* 2019) urges scientists to abandon the p < 0.05 binary altogether in favour of continuous interpretation.
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