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Measuring and data collection

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Statistical Handbook
Measuring and data collection
Choosing a statistical test
Minimum sample size
Not normally distributed
Statistical process control (SPC)


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For a good result out of the test data, proper data collection is essential. This starts with how to measure the data. Is the characteristic measurable or is only a derivation measurable. In the best case the measurement result is also the characteristic we want to know. But in many cases this is not the case due; cost, time or the possibility to measure.

Example of derivations

CW factor (air resistance) of a car: You are actually interested in fuel consumption or noise of the car.

Scales of the measurement

When choosing a measurement method it is important to look at the scale of the measurement data. The characteristic of the output is important for what kind of statistic test can be used.

Ratio

Output is a continuous scale with an absolute zero that is meaningful. You can use every fraction (or ratio) with a variable ratio.
Example: weight, speed, force, distance, resistance.

Statistics

Interval

Output is a continuous scale without a absolute zero. Like with Ratio you can use every fraction (or ratio) with a ratio variable. Without a true zero, it is impossible to compute ratios. Multiplying and dividing is not possible (20˚C is not twice as hot as 10˚C).

Statistics

Same tests as Ratio.

Ordinal

Output can be arranged in a rank order. But the interval distance does not have any meaning, like with interval and ratio.
Example: Good, better, best

Statistics

Nominal

Output can be put in different categories but there is no order.
Example: black - brown - red, Yes - No.

Statistics

Shape of the scale

When you have a Ratio or interval scale the shape is not always linear for instance with sound measurements the dBA is not linear but logarithmic. As result of this behavior the result may not be normally distributed and the data must be normalized or a Non parametric tests must be used.

Measuring

It is crucial to have a good measurement this makes the statistics more significant with smaller sample sizes.

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