The inverse cumulative distribution function gives the value associated with a specific cumulative probability. Use the inverse CDF to determine the value of the variable associated with a specific probability.
For example, an appliance manufacturer investigates failure times for the heating element within its toasters. They want to determine the time by which specific proportions of heating elements will fail so they can set the warranty period. Heating element failure times follow a normal distribution with a mean of 1000 hours and standard deviation of 300 hours. The probability density function (PDF) helps identify regions of higher and lower failure probabilities. The inverse CDF gives the corresponding failure time for each cumulative probability.
Use the inverse CDF to estimate the time by which 5% of the heating elements will fail, times between which 95% of all heating elements will fail, or the time at which only 5% of the heating elements remain. The inverse CDF for specific cumulative probabilities is equal to the failure time at the right side of the shaded area under the PDF curve.
 

The time by which 5% of the heating elements are expected to have failed is the inverse CDF of 0.05 or 507 hours.
The time by which 2.5% of the heating elements are expected to have failed is the inverse CDF of 0.025 or 412 hours.
Therefore, times between which 95% of all heating elements are expected to fail is the inverse CDF of 0.025 and the inverse CDF of 0.975 or 412 hours and 1588 hours.
 

The time at which only 5% of the heating elements are expected to remain is the inverse CDF of 0.95 or 1493 hours.
When you try to determine the inverse cumulative probability of a discrete distribution, the output contains two sets of columns.
Suppose you have the inverse cumulative probability of a proportion, p. The first set of columns in the output lists the largest x such that P(X ≤ x) ≤ p. The second set of columns lists the smallest x such that P(X ≤ x) ≥ p.
C1 

0 
1 
2 
A column of probabilities is stored in the worksheet.
C1  C2 

Binomial CDF for C1  
0  0.047553 
1  0.194622 
2  0.419775 
Now that you know the cumulative probabilities associated with the number of defectives, calculate the inverse cumulative probability.
Suppose that you want to calculate the number of defectives, x, such that the cumulative probability, p, is 0.50. From the previous results, you know that P(X ≤ 1 ) = 0.194622 and P(X ≤ 2 ) = 0.419775. Because the binomial distribution is a discrete distribution, the number of defectives cannot be between 1 and 2. In other words, you may have 1 defective or 2 defectives, but not 1.4 defectives.
 

The first probability indicates a value of x such that P(X ≤ x) < p and the second probability indicates the smallest x such that P(X ≤ x) ≥ p. In this example, the first probability shows the largest number of defectives, x=2, such that P(X≤2)<0.5 and the 2^{nd} shows the smallest number of defectives, x=3, such that P(X≤3) ≥ 0.5.
You can use Minitab to calculate a critical value for a hypothesis test instead of looking in a table.
Suppose that you perform a chisquare test with an α=0.02 and 12 degrees of freedom. What is the corresponding critical value? An α=0.02 corresponds to a cumulative probability value of 1  0.02 = 0.98.
Minitab displays the critical value, 24.054. For the chisquare test, if the test statistic is greater than the critical value, you can conclude that there is statistical evidence to reject the null hypothesis.
This example is for a chisquare distribution. However, you can use similar steps for other distributions.
 
