A review of discrete and continuous types of data and the differences between each type.

1. Section & Lesson #:
Pre-Requisite Lessons:
Complex Tools + Clear Teaching = Powerful Results
Data Types
Six Sigma-Measure – Lesson 7
A review of discrete and continuous types of data and the differences
between each type.
None
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted by any means
(electronic, mechanical, photographic, photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

2. Two Type of Data: Continuous & Discrete
o There are two primary types of data; below is a description of each:
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
Characteristics Continuous Data Discrete Data
Also known as Variable data Attribute data
Definition
Measured on a
continuum; virtually infinite
in scale or divisibility
Measured by counts or
classifications; limited in
scale and divisibility
General Examples
Numeric values like dollars,
time, distance, or degrees
Yes/No, Pass/Fail,
Colors, Locations, %’s
Business Examples
Revenue/Expenses, call
duration, product specs
People (names or roles), product types,
stores/offices, defects, % Performance
Advantages
Since it’s infinitely divisible, the
slightest defect can be detected
& measured. This also allows
for smaller sample sizes.
Due to its either/or quality, it’s usually
fast & easy to collect. It’s also easy to
understand since it can be measured on
smaller scales (like yes/no or 1 to 5).
Disadvantages
Can be more difficult to measure
which can require additional costs
to collect data. The scale of scrutiny
may be too granular or broad.
Data can be subjective.
It only measures defect count
and not severity. May require
a large sample size.
Statistical Impact
Better measurement of variation;
used with statistical tools that allow
for more precision (e.g., regressions)
Not very effective for measuring
variation; used with statistical tools
that are less precise (e.g., Chi2).

3. More Insight About Discrete Data
o How can discrete data include %’s since they’re numeric?
• %’s are actually a measure of discrete proportions.
• They’re discretely limited between 0% to 100%.
• Statistical tests can treat %’s like a continuous value, but use caution when doing so.
 Statistical tests don’t logically evaluate the types of data so it will accept %’s as continuous. The user
should understand the potential risks of misinterpreting the results that are intended for continuous data.
o There are two types of Discrete data:
• Count Data:
 Describes the frequency of an observable event or condition (e.g., a defect) that occurs within a process.
 Typically used at the unit level such as for measuring a defect rate.
• Classification Data:
 Describes the frequency of a characteristic attributable to a factor, part or output in a process.
 Typically used within the unit level for measuring proportional defectiveness or p(d).
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
3
5 Units:
Defects: Yes Yes Yes No Yes Defect Rate = 80% (4/5)
Defectives: 2/9 3/9 1/9 0/9 3/9 Defective Rate = 20% (9/45)

4. Practical Application
o Find at least 10 metrics used in your organization and answer the following for each:
• What kind of data type is it (i.e., discrete or continuous)?
• For any percentage metrics (which are numerical representations of discrete values), what are the
actual discrete values used to calculate the percent?
• For any discrete values, is there a way that same information could be measured as a continuous
value to allow for more advanced statistical analysis?
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
4