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Standardized Z Score Table (Standard Normal Table)

What is a standardized z score table, and how is it used to find cumulative probability, tail probability, and intervals for a normal distribution?

Subject: Statistics Chapter: Continuous Random Variables and the Normal Distribution Topic: The Standard Normal Distribution Answer included
standardized z score table standard normal table z table z-score standard score standard normal distribution cumulative probability normal CDF
Accepted answer Answer included

A standardized z score table (standard normal table) reports values of \(\Phi(z)=P(Z\le z)\) for the standard normal random variable \(Z\sim N(0,1)\), linking z-scores to cumulative probability, tail probability, and normal-distribution intervals.

Meaning of a standardized z score

A z-score standardizes a value \(x\) from a normal distribution \(X\sim N(\mu,\sigma^2)\) by expressing it in units of standard deviations: \[ z=\frac{x-\mu}{\sigma}. \] Under this standardization, \(Z=\frac{X-\mu}{\sigma}\) follows the standard normal distribution \(N(0,1)\).

Meaning of the standardized z score table

The most common standardized z score table lists the cumulative distribution function (CDF) of \(Z\): \[ \Phi(z)=P(Z\le z). \] Many printed tables display only \(z\ge 0\); negative z-scores are handled using symmetry of the normal curve.

Standard normal curve and a highlighted lookup in the standardized z score table Left panel shows the standard normal curve with the area to the left of z = 1.23 shaded (Phi(1.23)=0.8907). Right panel shows a small standard normal table snippet with row 1.2 and column 0.03 highlighted to match the shaded area. Standard normal curve with cumulative area Φ(z) Example lookup: z = 1.23 → Φ(1.23) = 0.8907 -3 -2 -1 0 1 2 3 z density mean 0 z = 1.23 shaded area = Φ(1.23) = 0.8907 Table indexing for Φ(z) = P(Z ≤ z) Row gives first two digits, column gives second decimal standardization z = (x − μ) / σ → use z in the standardized z score table z 0.00 0.01 0.02 0.03 0.04 1.2 0.8849 0.8869 0.8888 0.8907 0.8925 Highlighted cell matches z = 1.23 Φ(1.23) = 0.8907 gives the cumulative probability to the left selected lookup
The shaded region under the standard normal curve equals \(\Phi(1.23)\). The mini table highlights how z = 1.23 is located by combining row 1.2 with column 0.03 to obtain 0.8907.

Probability relationships used with z tables

Many standardized z score tables use the left-tail cumulative convention \(\Phi(z)=P(Z\le z)\). When a table uses a different convention (right tail or mean-to-z area), simple conversions map back to \(\Phi(z)\).

Quantity Expression in terms of \(\Phi(z)\) Interpretation
Cumulative (left tail) \(\Phi(z)\) Area to the left of \(z\)
Right tail \(P(Z\ge z)=1-\Phi(z)\) Area to the right of \(z\)
Mean to z (for \(z\ge 0\)) \(P(0\le Z\le z)=\Phi(z)-\tfrac12\) Area between 0 and \(z\)
Symmetry for negatives \(\Phi(-z)=1-\Phi(z)\) Reflection about 0
Central two-sided probability \(P(-z\le Z\le z)=2\Phi(z)-1\) Area between \(-z\) and \(z\)

Worked probabilities using the standardized z score table

For \(Z\sim N(0,1)\) and \(z=1.23\), the table entry \(\Phi(1.23)=0.8907\) gives \(P(Z\le 1.23)=0.8907\). The complementary right-tail probability satisfies \(P(Z\ge 1.23)=1-0.8907=0.1093\).

For a nonstandard normal variable \(X\sim N(\mu,\sigma^2)\), the standardization \(z=(x-\mu)/\sigma\) converts a probability statement about \(X\) into one about \(Z\). As an illustration, if \(X\sim N(50,10^2)\) and \(x=62.3\), then \[ z=\frac{62.3-50}{10}=1.23, \qquad P(X\le 62.3)=P\!\left(Z\le 1.23\right)=\Phi(1.23)=0.8907. \]

Interval probabilities use two z-scores. If \(X\sim N(50,10^2)\) and the event is \(45

Critical z values and inverse lookup

Many hypothesis tests and confidence intervals require a critical value \(z_\alpha\) satisfying \(P(Z\ge z_\alpha)=\alpha\), equivalently \(\Phi(z_\alpha)=1-\alpha\). A common two-sided 95% level corresponds to \(\alpha/2=0.025\), giving \(\Phi(z)=0.975\) and \(z\approx 1.96\).

Level Cumulative probability \(\Phi(z)\) Approximate z Right tail \(1-\Phi(z)\)
90% one-sided 0.9000 1.282 0.1000
95% one-sided 0.9500 1.645 0.0500
95% two-sided (central) 0.9750 1.960 0.0250
99% two-sided (central) 0.9950 2.576 0.0050

Standardized z score table for \(\Phi(z)\) with \(z\ge 0\)

The table below lists \(\Phi(z)=P(Z\le z)\) for \(Z\sim N(0,1)\) and nonnegative z-scores. Negative values use \(\Phi(-z)=1-\Phi(z)\).

Standardized z score table (cumulative \(\Phi(z)\))

Row labels give \(z\) to one decimal place. Column labels add the second decimal place. Example: row 1.2 and column 0.03 correspond to \(z=1.23\).

z0.000.010.020.030.040.050.060.070.080.09
0.00.50000.50400.50800.51200.51600.51990.52390.52790.53190.5359
0.10.53980.54380.54780.55170.55570.55960.56360.56750.57140.5753
0.20.57930.58320.58710.59100.59480.59870.60260.60640.61030.6141
0.30.61790.62170.62550.62930.63310.63680.64060.64430.64800.6517
0.40.65540.65910.66280.66640.67000.67360.67720.68080.68440.6879
0.50.69150.69500.69850.70190.70540.70880.71230.71570.71900.7224
0.60.72570.72910.73240.73570.73890.74220.74540.74860.75170.7549
0.70.75800.76110.76420.76730.77040.77340.77640.77940.78230.7852
0.80.78810.79100.79390.79670.79950.80230.80510.80780.81060.8133
0.90.81590.81860.82120.82380.82640.82890.83150.83400.83650.8389
1.00.84130.84380.84610.84850.85080.85310.85540.85770.85990.8621
1.10.86430.86650.86860.87080.87290.87490.87700.87900.88100.8830
1.20.88490.88690.88880.89070.89250.89440.89620.89800.89970.9015
1.30.90320.90490.90660.90820.90990.91150.91310.91470.91620.9177
1.40.91920.92070.92220.92360.92510.92650.92790.92920.93060.9319
1.50.93320.93450.93570.93700.93820.93940.94060.94180.94290.9441
1.60.94520.94630.94740.94840.94950.95050.95150.95250.95350.9545
1.70.95540.95640.95730.95820.95910.95990.96080.96160.96250.9633
1.80.96410.96490.96560.96640.96710.96780.96860.96930.96990.9706
1.90.97130.97190.97260.97320.97380.97440.97500.97560.97610.9767
2.00.97720.97780.97830.97880.97930.97980.98030.98080.98120.9817
2.10.98210.98260.98300.98340.98380.98420.98460.98500.98540.9857
2.20.98610.98640.98680.98710.98750.98780.98810.98840.98870.9890
2.30.98930.98960.98980.99010.99040.99060.99090.99110.99130.9916
2.40.99180.99200.99220.99250.99270.99290.99310.99320.99340.9936
2.50.99380.99400.99410.99430.99450.99460.99480.99490.99510.9952
2.60.99530.99550.99560.99570.99590.99600.99610.99620.99630.9964
2.70.99650.99660.99670.99680.99690.99700.99710.99720.99730.9974
2.80.99740.99750.99760.99770.99770.99780.99790.99790.99800.9981
2.90.99810.99820.99820.99830.99840.99840.99850.99850.99860.9986
3.00.99870.99870.99870.99880.99880.99890.99890.99890.99900.9990
3.10.99900.99910.99910.99910.99920.99920.99920.99920.99930.9993
3.20.99930.99930.99940.99940.99940.99940.99940.99950.99950.9995
3.30.99950.99950.99950.99960.99960.99960.99960.99960.99960.9997
3.40.99970.99970.99970.99970.99970.99970.99970.99970.99970.9998

Numerical caution for extreme z-scores

For large \(|z|\), the normal tail probability becomes very small. Table rounding can mask differences in extreme tails; higher-precision computation is preferred when \(1-\Phi(z)\) is near the table’s rounding threshold.

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