Glossary
Whether you’re new to the quality and continuous improvement arena or a seasoned expert seeking detailed information on a specific quality topic, you should find what you need in our Glossary of Terms.
Explore the concepts, tools, applications and technical terms that make up the world of continuous improvement.
HACCP
A term used to describe Hazard Analysis and Critical Control Points which are points considered critical for evaluation of potential sources of failure in a product under design.
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Half Interval Test
When applying the Half Interval Width rule you are seeking to add data confidence to your testing to tempering both excessive skepticism and overstated usability findings. As such, confidence intervals make testing more efficient by quickly revealing unusable test with very small samples.
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Hawthorn Effect
A term used to refer to a tendency found in some people who work more productively when they are participants in an experiment. It has been found that individuals may change their behavior due to the perceived attention they are receiving from researchers rather than because of any manipulation. Researchers at Harvard University who were studying the relationship between productivity and work environment first discovered the effect.
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Heijunka
Heijunka is a Japanese term for levelling the type and quantity of production over a fixed period of time thus enabling production to efficiently meet customer demand while avoiding large batch sizes.
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Hidden Factory
The Hidden Factory is a term used to expresses the notion that much of what an organisation does when they are not committed to Quality is directed inadvertently to creating waste and performing wasteful tasks. The Hidden Factory therefore, is the calculated potential output that would theoretically be possible if the efforts of the business were redirected from waste generating towards implementing continuous improvement programs that result in quality improvement.
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Histogram
A Histogram is a form of vertical bar chart that illustrates the distribution of numerical data, (either discrete or continuous) according to the frequency of occurrence on a continuous scale. Histograms are suitable for measuring variables where values are numerical and measured on an interval scale. They are most commonly used when dealing with large data sets, of more than 100 observations and are effective charts for identifying Outliers and missing data.
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Honeycombing
Honeycombing is a term used to depict the condition of empty spaces within a stock storage system usually as the result of partial pallet loads or depleted rows. The Honeycombing factor cannot be calculated simply by dividing theoretical capacity into actual capacity, as there are many other reasons why empty space may exist within a store or warehouse.
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House of Quality
House of Quality is a graphic tool for defining the relationship between customer expectations and the capabilities of the organisation to deliver to those expectations. The tool is presented as a matrix of Customer Requirements multiplied by Product over Service Features, which are then indicated on the intersections of the matrix as the degree to which the particular feature contributes to meeting customer requirements. Once determined a detailed version of the matrix can then be drawn up in the form of a block diagram bearing references to the original matrix. The diagrammatic form of the block diagram, comprising blocks on top of other blocks, have lead to its being known as the House of Quality.
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Hypergeometric Distribution
The Hypergeometric Distribution arises when a random selection, without repetition is made among objects of two distinct types. The Hypergeometric Distribution is described by three parameters, (N) the total number of objects, (R) the number of objects of the first type, and (k) the number of objects to be chosen.
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Hypothesis Test
Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps, (1) to Formulate the Null Hypothesis, (2) to identify a Test Statistic that can be used to assess the truth of the Null Hypothesis, (3) to Compute the p-value which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the Null Value be true and, (4) to Compare the p-value to an acceptable significance value, known as the Alpha Value.