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Quantitative measurements are those which involve the collection of numbers. It is the opposite of qualitative data which are observations. For example, if you were interested in looking at height. Quantitative measurements would be taking an accurate measurement of everyone. Qualitative data would be looking at the person and putting them into a category of 'tall,' 'medium,' 'short.'

Q: What are quantitative measurements?

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Recorded observations and measurements from an experiment are referred to as data. The data can either be quantitative or qualitative.

5 examples of quantitative measurement are:Weight of apples.Dollars in bank accounts.Length of bolts.Number of students in classrooms.Number of cars in a parking lot.

It is a more logical system, where everything is divisible by 10. With other units, everything is arbitrary.

Quantitative data is data that measures quantity, as opposed to qualitative data which describes quality. Some examples of quantitative data pertaining to weather would be: measurements of precipitation, records of number of days per month without precipitation, percentage of the chance of precipitation, records of daily high temperatures.

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Measurements ;)

Quantitative is based on measurements and numbers :)

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A quantitAtive observation

Experiments are often likely to contain errors. Quantitative error analysis means determining uncertainty, precision and error in quantitative measurements.

Experiments are often likely to contain errors. Quantitative error analysis means determining uncertainty, precision and error in quantitative measurements.

Recorded observations and measurements from an experiment are referred to as data. The data can either be quantitative or qualitative.

An observation is quantitative if it has something to do with the amount of the substance or measurements.

Quantitative error analysis is the process of quantifying uncertainties in measurement data to determine the reliability and precision of the measurements. It involves identifying sources of error, calculating error propagation through calculations, and estimating the overall uncertainty in the final result. This helps in understanding and improving the accuracy of experimental measurements.