SPC Glossary 

Your quick reference to statistical process control for manufacturing quality managment systems.

Quality Management System Glossary:
L, M, N, O



Also known as a Batch.
  1. A defined quantity of product accumulated under conditions considered uniform for sampling purposes.
  2. Items constituting a defined quantity of uniform product for purposes of proceeding collectively through a process.

Lot Quality

The value of percentage defective or defects per hundred units in a lot.

Lot Size 

Also referred to as N.

The number of units in a lot.

Lot Tolerance Percentage Defective (LTPD)



Lower Control Limit (LCL) 

Control limit for points below the central line in a Control Chart.



The arithmetic average of a discrete set of values in a data set.


The criteria, metric, or means to which a comparison is made with output.



Measurement System

All operations, procedures, devices, and other equipment, personnel and environment used to assign a value to the characteristic being measured.

Measurement Uncertainty





The value occurring most frequently in a data set.

Moving Range

A measure used to help calculate the variance of a data population; the distance or difference between consecutive points. The moving range chart is typically used with an Individual X (IX) chart for single measurements.

Moving Sigma

A measure used to calculate variation using the standard deviation between two consecutive points from an IX control chart. The calculations are then plotted and analyzed on a time-ordered Moving-s control chart.

Multivariate Control Chart




The number of units in a sample.


The number of units in a population.

Nonconforming Unit

A unit with one or more nonconformities or defects. Also called a reject.


A specified requirement that is not fulfilled. Also see Blemish, Defect, and Imperfection.

Nondestructive Testing and Evaluation (NDT, NDE)

Testing and evaluation methods that do not damage or destroy the test specimen.

Nonparametric Tests

All tests involving ranked data (data that can be put in order). Nonparametric tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable.

Normal Distribution (statistical)

The charting of a data set in which most of the data points are concentrated around the average (mean), thus forming a bell-shaped curve.


A control chart based on counting the number of defective units in each constant size subgroup. The np-chart is based on the binomial distribution.


Operating Characteristic Curve (OC curve)

Also known as Operating Curve.

A graph to determine the probability of accepting lots as a function of the lots’ or processes’ quality level when using various sampling plans. There are three types: type A curves, which give the probability of acceptance for an individual lot coming from finite production (will not continue in the future); type B curves, which give the probability of acceptance for lots coming from a continuous process; and type C curves, which (for a continuous sampling plan) give the long-run percentage of product accepted during the sampling phase.


Unusually large or small observations relative to the rest of the data.

Out-of-Control Process

A process in which the statistical measure being evaluated is not in a state of statistical control. In other words, the variations among the observed sampling results cannot be attributed to a constant system of chance causes. Also see In-Control Process.


A term that indicates a unit does not meet a given requirement or specification.

Overall Equipment Effectiveness (OEE)

Used to measure manufacturing productivity; identifies the percentage of manufacturing time that is truly productive. An OEE score of 100% means you are manufacturing only Good Parts, as fast as possible, with no Stop Time. In the language of OEE that means 100% Quality (only Good Parts), 100% Performance (as fast as possible), and 100% Availability (no Stop Time).

Over Control

An element often introduced into a process by a well-meaning operator or controller who considers any appreciable deviation from the target value as a special cause. In this case, the operator is incorrectly viewing common-cause variation as a fault in the process. Over control of a process can actually increase the variability of the process and is viewed as a form of tampering.

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