Minitab provides eight tests for special causes. Experts recommend that you use both Test 1 and Test 2 when you create a T chart because the T chart may be slow to detect small to moderate decreases in the average time between events. Select additional tests based on company or industry standards. Use the tests to determine which observations to investigate, and to identify the specific patterns and trends in your data.

For the traditional control charts for variables data, Tests 1, 5, 6, 7, and 8 are based on the normal distribution. However, for T charts, these tests are based on the Weibull distribution or the exponential distribution. For example, a point on a T chart fails Test 1 when the point is outside of the percentile range that correspond to 3 standard deviations from the mean for a normal distribution.

In the drop-down list, specify whether to perform some, all, or no tests for special causes. You can make each test more or less sensitive by changing the value of K.

To change the default settings for future sessions of Minitab, choose .

- 1 point > K standard deviations from center line
- Test 1 identifies subgroups that are unusual compared to other subgroups. Test 1 is universally recognized as necessary for detecting out-of-control situations. If small shifts in the process are of interest, you can use Test 2 to supplement Test 1 in order to create a control chart that has greater sensitivity.
- K points in a row on same side of center line
- Test 2 identifies shifts in the process centering or variation. If small shifts in the process are of interest, you can use Test 2 to supplement Test 1 in order to create a control chart that has greater sensitivity.
- K points in a row, all increasing or all decreasing
- Test 3 detects trends. This test looks for a long series of consecutive points that consistently increase in value or decrease in value.
- K points in a row, alternating up and down
- Test 4 detects systematic variation. You want the pattern of variation in a process to be random, but a point that fails Test 4 might indicate that the pattern of variation is predictable.
- K out of K+1 points > 2 standard deviations from center line (same side)
- Test 5 detects small shifts in the process.
- K out of K+1 points > 1 standard deviation from center line (same side)
- Test 6 detects small shifts in the process.
- K points in a row within 1 standard deviation of center line (either side)
- Test 7 detects a pattern of variation that is sometimes mistaken as evidence of good control. This test detects control limits that are too wide. Control limits that are too wide are often caused by stratified data, which occur when a systematic source of variation is present within each subgroup.
- K points in a row > 1 standard deviation from center line (either side)
- Test 8 detects a mixture pattern. In a mixture pattern, the points tend to fall away from the center line and instead fall near the control limits.