Biology-Practical terms

Here are the terms which appear to confuse many students: validity, reliability, accuracy, sensitivity and precision/exactness. Although we may colloquially use these terms interchangeably, in the scientific context, all these terms have big differences in usage.

Let me clarify them for you:

A] Validity

Validity refers to whether a study us able to scientifically answer the questions it is intended to answer. The experimenter succeeds in getting the intended (expected, scientifically predicted) result/ manages to answer the scientific problem correctly if the study is valid.

A valid investigation is one that is properly designed to answer the question that is being asked.

In a valid experiment:

1. All fixed variables kept constant.
2. Manipulated variable is correctly controlled with proper techniques.
3. Responding variable is measured with correct method.
4. Minimizes random error (by replication or proper control of all variables)
5. Minimizes systematic error (by proper instruments or avoiding personal bias)
6. Repeatable and hence, reliable.
7. Special measures taken to ensure reliability, accuracy, precision, minimize environmental influence, bias, safety.

(Note: Sometimes, when the question in Edexcel Biology asks you to describe/ set-up an experiment to investigate something, what they really meant is how you are going to set up a VALID experiment. Which is why we write our answers using the guidelines above.)

Internal validity dictates the degree of validity of how an experimental design is structured and encompasses all of the steps of the scientific research method. Even if your results are great, sloppy, questionable and inconsistent design will compromise your integrity in the eyes of the scientific community. Internal validity and reliability are at the core of any experimental design.

External validity is the process of examining the results and questioning whether there are any other possible causal relationships. Control groups and randomization will lessen external validity problems but no method can be completely successful. This is why the statistical proofs of a hypothesis called significant, not absolute truth. Any scientific research design only puts forward a possible cause for the studied effect. There is always the chance that another unknown factor contributed to the results and findings. This extraneous causal relationship may become more apparent, as techniques are refined and honed.

Validity defines the strength and scientific basis of the final results and whether they can be regarded as accurately describing the real world, gives strong scientific basis for a theory or hypothesis.

Another common definition of validity in terms of accuracy and precision: A study is called valid if it is both accurate (true and correct) and precise (referring to closeness of repeated results).

Higher validity means small difference between recorded results and the actual value, is repeatable and the repeats are close to each other.

B] Reliability

The idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable. Other researchers must be able to perform exactly the same experiment, under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis.

However, diligent scientists take measurements many times, to minimize the chances of malfunction and maintain validity and reliability. At the other extreme, any experiment that uses human judgment is always going to come under question. Human judgment can vary wildly between observers, and the same individual may rate things differently depending upon time of day and current mood. This means that such experiments are more difficult to repeat and are inherently less reliable. Reliability is a necessary ingredient for determining the overall validity of a scientific experiment and enhancing the strength of the results.

Reliability, in simple terms, describes the repeatability and consistency of a test.


Valid data is evidence that is BOTH reliable and which is relevant to the question being investigated. For example, a student decides to carry out a study to investigate the effect of alcohol on daphnia heartbeat. If the results obtained are replicated many times to give similar results, it is reliable. However, it is given that the result apparently shows that higher concentration of alcohol increases heartbeat, when in reality it should decrease. Therefore, the experiment has been unsuccessful in getting the intended/ scientifically correct answer. It is invalid although the result falsely shows that it is reliable.

C] Accuracy

• Closeness of a value to the true value
• It is affected by systematic error and personal bias
• Systematic errors are errors that produce a result that differs from the true value by a fixed amount. These errors result from biases introduced by error in instrumental method, incorrect calibration, imperfect methods of observation, zero error or human factors.
• An example of an instrumental bias is an incorrectly calibrated pH meter that shows pH values 0.5 units lower than the true value. An example of a method error would be partial loss of a volatile analyte during the ashing step in graphite furnace atomic absorption (AA) spectroscopy. An example of human bias is a student who records titration endpoints beyond the true endpoint due to colour blindness. An example of imperfect method of observation would be to estimate absorbance using naked eye instead of colorimeter.
• Systematic errors can be identified and corrected by analyzing standards that closely match the real sample.
• Systematic errors can be identified by comparing results with a known standard (example: data booklet values, manufacturer’s nutritional information) and reduced by calibration and refining method of observation.
• Accuracy can be increased by more accurate instruments.

D] Precision/ Consistency

• A measure of consistency/exactness: the repeated results are close to each other.
• Measures closeness of repeated measurements to each other.
• Repetition and then calculating the mean or increasing sample size is done to minimize random errors.
• Another method to reduce random error is to use more precise equipment. 
• The higher the precision of a measurement instrument, the smaller the variability (standard deviation) of the fluctuations in its readings.
• Random error: values lying above or below a true value / mean value.

E] Sensitivity

Refers to ability of an instrument to respond to a small change in reading. However, a sensitive instrument may not always be accurate in its readings. For example, a vernier caliper may measure up to 0.1mm, but if there is zero error, then the accuracy is compromised by this systematic error.

BASIC QUESTIONS to determine which term to use:

1. Is the result trustworthy? Can I believe if it is truly tested and will give the same results if I repeat it? (Reliability)
2. How close is it to the true value? (Accuracy)
3. Are the repeated results consistent? (Consistency/ Precision/ Exactness)
4. Is the study accurate (true to the real fact and scientifically correct), precise (the results when repeated are close to each other), and reliable (gives same results when repeated under same conditions)? Does the study correctly answer the question? (for example, do the results of the experiment tell me that higher temperature causes higher rate of reaction?) (Validity)