Inverse Z-Score Calculator
Based on the standard normal distribution (μ = 0, σ = 1)
Based on the standard normal distribution (μ = 0, σ = 1)
Confidence Interval
The most cited critical value in statistics. Used for two-sided 95% confidence intervals and α = 0.05 two-tailed tests.
±1.96 multiplied by the standard error gives the half-width of a 95% confidence interval.
Hypothesis Testing
A common upper-tail rejection threshold for one-sided tests where you only care about deviations in one direction.
1.6449 is the standard one-tailed 5% critical z, distinct from the two-tailed value of 1.96.
Percentile
Use this when you have a percentile rank and need to translate it into a z-score for further calculation.
Multiply this z-score by σ and add μ to find the equivalent raw value in any normal distribution.
The inverse normal CDF — also called the probit or quantile function — returns the z-score that has a given cumulative probability under the standard normal distribution. Φ⁻¹(0.95) ≈ 1.6449 means 95% of standard normal values lie at or below z = 1.6449.
z = Φ⁻¹(p)
An inverse z-score calculator runs the standard normal distribution in reverse. Instead of starting with a z-score and looking up its probability, you start with a probability, alpha level, or confidence percentage and find the z-score that produces it. This is the value statisticians call the critical value. The calculation uses the inverse normal CDF (the quantile or probit function), which is the mathematical inverse of the cumulative probability you would read off a z-table. Pick the mode that matches the question you are asking — left-tail probability, right-tail probability, two-tailed alpha, or confidence level — and the calculator returns the z-score that bounds the area you specified.
A researcher wants the critical z-score for a two-tailed test at α = 0.05. What is the cutoff value beyond which a result is considered statistically significant?
This is why ±1.96 is the universally cited critical value for two-tailed 95% tests — it is the inverse z-score for α = 0.05 split across two symmetric tails.
The inverse normal CDF answers the question: which z-score has this probability? It complements the forward normal CDF, which answers: what is the probability for this z-score? Together they form a two-way map between standardized scores and tail areas. Critical values are inverse z-scores commonly used as decision thresholds in hypothesis testing and confidence intervals. The most common ones are 1.6449 (one-tailed 95%), 1.96 (two-tailed 95%), 2.3263 (one-tailed 99%), and 2.5758 (two-tailed 99%).
For a two-sided 95% confidence interval, the critical z-score is ±1.96. For a one-sided 95% bound, it is approximately 1.6449.
For a two-sided 99% interval, the critical z-score is ±2.5758. For a one-sided 99% bound, it is about 2.3263.
A critical z-score is a fixed cutoff calculated from your alpha or confidence level. A test statistic is the z-score computed from your sample data. You compare the test statistic to the critical z-score to decide whether to reject the null hypothesis.
There is no closed-form formula. Most calculators use a high-accuracy rational approximation such as Acklam's algorithm. This calculator uses Acklam's approximation, accurate to about 1.15 × 10⁻⁹.
A two-tailed test rejects the null hypothesis when the test statistic falls into either tail. To keep the total Type I error at α, half of α is allocated to the left tail and half to the right tail.
For probability and alpha modes, the value must be strictly between 0 and 1. For confidence level, the value must be strictly between 0 and 100. The inverse CDF diverges to infinity at the boundaries.
The 90th percentile corresponds to a left-tail probability of 0.90, which gives a critical z-score of approximately 1.2816.
Use the right-tail mode with p = 0.01. The result is z ≈ 2.3263, the standard one-tailed 1% significance critical value.
Reference: The inverse normal CDF (probit) is computed using Peter Acklam's rational approximation, which has accuracy better than ~1.15 × 10⁻⁹ across the full unit interval.