There are two data types: 1. Attribute 2. Continuous. Attribute data has countable quality characteristics for example, number of defects, Number of defectives, Number of NCs, etc. Continuous data on the other hand has measurable quality characteristics. For example, length of a spark plug, weight of a spark plug, temperature at which the spark plug has maximum efficiency, etc.
If a software project just collects data on whether each milestone is met or not met, it is collecting attribute data. This does not tell us whether we have overshot or under met the expectations.
Another example that shows the difference between attribute and continuous data: In a glass (drinking water glass) manufacturing industry, there are two teams, which assure that length of the glass is of stipulated length. The first team uses Vernier Calipers to measure the length. If the glass is of stipulated length, it passes the quality check, otherwise not. This type where the length of glass is MEASURED, is called Continuous data. The second team uses the go-noGO gauge technique. Here, the glass is allowed to pass through two separate raised platforms. The first platform has allows glass of stipulated length, while the second one allows only shorter. The inspection items are passes thru both the platforms one after another. If any glass passes thru both of them, then it is of shorter length than desired. If it does not pass thru any of the two platforms, it is of longer length. This way of gauging relies on ATTRIBUTE data, because the team checks for Yes/No condition for each glass.
Attribute data does not need costly implements. In our example, the second method is far cheaper than engaging vernier calipers, but we lose a lot of detail.
Note: Difference between defects, and defectives. “Defects” is the total number of defects in all the pieces inspected. “Defectives” is the count of items which have defects. For example, in a water glass manufacturing industry, in a lot of 100, these defects were found in one inspected item: the length of the glass is improper, has cracks. In another inspected item these defects were found: shape was malformed.
So, out of 100, the defectives here are 2 glasses, while the defects are three (for glass 1, length and cracks, and glass 2 the malformed shape). “Defects” therefore is always a better representative of the abnormalities / deviations, than “Defectives”.
Note that continuous data can be converted to attribute data, but vice versa. So, it is always better to go for continuous data if there is a possibility to measure it.
If a software project just collects data on whether each milestone is met or not met, it is collecting attribute data. This does not tell us whether we have overshot or under met the expectations.
Another example that shows the difference between attribute and continuous data: In a glass (drinking water glass) manufacturing industry, there are two teams, which assure that length of the glass is of stipulated length. The first team uses Vernier Calipers to measure the length. If the glass is of stipulated length, it passes the quality check, otherwise not. This type where the length of glass is MEASURED, is called Continuous data. The second team uses the go-noGO gauge technique. Here, the glass is allowed to pass through two separate raised platforms. The first platform has allows glass of stipulated length, while the second one allows only shorter. The inspection items are passes thru both the platforms one after another. If any glass passes thru both of them, then it is of shorter length than desired. If it does not pass thru any of the two platforms, it is of longer length. This way of gauging relies on ATTRIBUTE data, because the team checks for Yes/No condition for each glass.
Attribute data does not need costly implements. In our example, the second method is far cheaper than engaging vernier calipers, but we lose a lot of detail.
Note: Difference between defects, and defectives. “Defects” is the total number of defects in all the pieces inspected. “Defectives” is the count of items which have defects. For example, in a water glass manufacturing industry, in a lot of 100, these defects were found in one inspected item: the length of the glass is improper, has cracks. In another inspected item these defects were found: shape was malformed.
So, out of 100, the defectives here are 2 glasses, while the defects are three (for glass 1, length and cracks, and glass 2 the malformed shape). “Defects” therefore is always a better representative of the abnormalities / deviations, than “Defectives”.
Note that continuous data can be converted to attribute data, but vice versa. So, it is always better to go for continuous data if there is a possibility to measure it.
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