How Accurate Do Scientist Need To Be When Collecting Data

What is a measurement?

A measurement tells you lot about a property of something you are investigating, giving it a number and a unit of measurement. Measurements are always fabricated using an instrument of some kind. Rulers, stopclocks, chemical balances and thermometers are all measuring instruments.

Some processes seem to exist measuring, simply are non, e.g. comparison two lengths of string to see which i is longer. Tests that lead to a simple aye/no or pass/fail result do not always involve measuring.

The quality of measurements

Evaluating the quality of measurements is an essential step on the way to sensible conclusions. Scientists use a special vocabulary that helps them think clearly about their data. Key terms that depict the quality of measurements are:

Validity
Accurateness
Precision (repeatability or reproducibility)
Measurement uncertainty

Validity: A measurement is 'valid' if it measures what it is supposed to be measuring. What is measured must also exist relevant to the question being investigated.

If a factor is uncontrolled, the measurements may not be valid. For instance, if yous were investigating the heating consequence of a current ( P = I ² R) by increasing the current, the resistance of the wire may change as it is heated by the current to different temperatures. This would skew the results.

Right conclusions can only be drawn from valid information.

Accurateness: This describes how closely a measurement comes to the true value of a physical quantity. The 'true' value of a measurement is the value that would be obtained by a perfect measurement, i.e. in an platonic world. As the true value is non known, accurateness is a qualitative term just.

Many measured quantities accept a range of values rather than 1 'truthful' value. For example, a collection of resistors all marked i kΩ. will have a range of values, merely the hateful value should be i kΩ.. You lot can have more confidence in a number of measurements of a sample rather than an individual measurement. The variation enables you to identify a mean, a range and the distribution of values across the range.

Precision: The closeness of understanding between replicate measurements on the same or like objects under specified conditions.

Repeatability or reproducibility (precision): The extent to which a measurement replicated under the same conditions gives a consistent issue. Repeatability refers to data collected by the same operator, in the same lab, over a curt timescale. Reproducibility refers to data collected by dissimilar operators, in unlike laboratories. Y'all tin have more confidence in conclusions and explanations if they are based on consistent data.

Measurement uncertainty: The dubiety of a measurement is the doubt that exists about its value. For any measurement – fifty-fifty the most careful – there is e'er a margin of doubt. In everyday speech, this might exist expressed as 'give or take…', east.g. a stick might be two metres long 'give or have a centimetre'.

The doubt about a measurement has 2 aspects:

the width of the margin, or 'interval'. This is the range of values ane expects the true value to lie within. (Notation this is not necessarily the range of values i might obtain when taking measurements of the value, which may include outliers.)
confidence level', i.due east. how sure the experimenter is that the true value lies within that margin. Give-and-take of conviction levels is generally appropriate only in advanced level science courses.

Uncertainty in measurements can be reduced by using an instrument that has a calibration with smaller calibration divisions. For example, if you use a ruler with a centimetre calibration then the incertitude in a measured length is likely to be 'give or take a centimetre'. A ruler with a millimetre scale would reduce the dubiety in length to 'give or take a millimetre'.

Measurement errors

It is important non to misfile the terms 'error' and 'uncertainty'. Error refers to the difference between a measured value and the true value of a concrete quantity existence measured. Whenever possible we try to right for any known errors: for example, past applying corrections from scale certificates. But any mistake whose value we practise not know is a source of uncertainty.

Measurement errors can arise from two sources:

a random component, where repeating the measurement gives an unpredictably unlike result;
a systematic component, where the same influence affects the result for each of the repeated measurements.

Every time a measurement is taken under what seem to be the same conditions, random effects can influence the measured value. A series of measurements therefore produces a besprinkle of values virtually a mean value. The influence of variable factors may change with each measurement, changing the hateful value. Increasing the number of observations generally reduces the uncertainty in the hateful value.

Systematic errors (measurements that are either consistently too large, or as well minor) can event from:

poor technique (e.m. carelessness with parallax when sighting onto a scale);
zero error of an instrument (e.thou. a ruler that has been shortened by wear at the zero end, or a newtonmeter that reads a value when zilch is hung from it);
poor calibration of an instrument (e.one thousand. every volt is measured besides large).

Whenever possible, a good experimenter volition endeavor and right for systematic errors, thus improving accuracy. For example, if it is known that a rest always reads ii g greater than the truthful reading it is perfectly possible to compensate for that error by simply subtracting 2 g from all readings taken.

Sometimes you tin can only discover a systematic error by measuring the same value past a different method.

Errors that are not recognized contribute to measurement uncertainty.

ASE/Nuffield booklet: The Language of Measurement

In 2010, following a series of meetings with Awarding Organisations, the ASE and Nuffield Foundation jointly published a booklet to enable teachers, publishers, awarding bodies and others in England and Wales to achieve a common understanding of key terms that ascend from applied piece of work in secondary science. Club a copy or see extracts from the booklet

The Linguistic communication of Measurement

Acknowledgement

This webpage is based on the National Physical Laboratory's Adept Exercise Guide: A Beginner's Guide to Doubtfulness of Measurements written by Stephanie Bong.

A Beginner's Guide to Uncertainty of Measurements