In my training workshops on decision rule for making statement of conformity after laboratory analysis of a product, some participants have found the subject of hypothesis testing rather abstract. But in my opinion, an understanding of the significance of type I and type II error in hypothesis testing does help to formulate decision rule based on acceptable risk to be taken by the laboratory in declaring if a product tested conforms with specification.

As we know well, a hypothesis is a statement that might, or might not, be true until we put it to some statistical tests. As an analogy, a graduate studying for a Ph.D. degree always carries out research works on a certain hypothesis given by his or her supervisor. Such hypothesis may or may not be proven true at the conclusion. Of course, a breakthrough of the research in hand means that the original hypothesis, called null hypothesis is not rejected.

In statistics, we set up the hypothesis in such as way that
it is possible to calculate the probability (*p*) of the data, or the test
statistic (such as Student’s t-tests) calculated from the data, given the
hypothesis, and then to make a decision about whether this hypothesis is to be
accepted (high *p*) or rejected (low *p*).

In conformity testing, we treat the specification or regulatory
limit given as the ‘true’ or certified value and our measurement value obtained
is the data for us to decide whether it conforms with the specification. Hence, our null hypothesis H_{o} can
be put forward as that there is no real difference between the measurement and
the specification. Any observed difference arises from random effects only.

To make decision rule on conformance in significance testing, a choice about the value of the probability below which the null hypothesis is rejected, and a significant difference concluded, must be made. This is the probability of making an error of judgement in the decision.

If the probability that the data are consistent with the
null hypothesis H_{o} falls below a pre-determined low value (say, alpha
= 0.05 or 0.01), then the hypothesis is rejected at that probability. Therefore, a *p*<0.05 would mean that
we reject H_{o} with 95% level of confidence (or 5% error) if the
probability of the test statistic, given the truth of H_{o}, falls
below 0.05. In other words, if H_{o}
were indeed correct, less than 1 in 20 repeated experiments would fall outside
the limits. Hence, when we reject H_{o}, we conclude that there was a
significant difference between the measurement and the specification limit.

Gone are the days when we provide a conformance statement when the measurement result is exactly on the specification value. By doing so, we are exposed to a 50% risk of being found wrong. This is because we either have assumed zero uncertainty in our measurement (which cannot be true) or the specification value itself has encompassed its own uncertainty which again is not likely true.

Now, in our routine testing, we would have established the
measurement uncertainty (MU) of test parameter such as contents of oil,
moisture, protein, etc. Our MU as an expanded uncertainty has been evaluated by
multiplying a coverage factor (normally *k* = 2) with the combined
standard uncertainty estimated, with 95% confidence. Assuming the MU is constant in the range of
values tested, we can easily determine the critical value that is not significantly
different from the specification value or regulatory limit by the use of
Student’s *t*-test. This is Case B
in the Fig 1 below.

So, if the specification has an upper or maximum limit, any test value smaller than the critical value below the specification estimated by the Student’s t-test can be ‘safely’ claimed to be within specification (Case A). On the other hand, any test value larger than this critical value has reduced our confidence level in claiming within specification (Case C). Do you want to claim that the test value does not meet with the specification limit although numerically it is smaller than the specification limit? This is the dilemma that we are facing today.

The ILAC Guide G8:2009 has suggested to state “not possible to state compliance” in such situation. Certainly, the client is not going to be pleased about it as he has used to receive your positive compliance comments even when the measurement result is exactly on the dot of the upper limit.

That is why the ISO/IEC 17025:2017 standard has required the accredited laboratory personnel to discuss his decision rule with the clients and get their written consent in the manner of reporting.

To minimize this awkward situation, one remedy is to reduce your measurement uncertainty range as much as possible, pushing the critical value nearer to the specification value. However, there is always a limit to do so because uncertainty of measurement always exists. The critical reporting value is definitely going to be always smaller than the upper limit numerically in the above example.

Alternatively, you can discuss with the client and let him provide you his acceptance limits. In this case, your laboratory’s risk is minimized greatly as long as your reported value with its associated measurement uncertainty is well within the documented acceptance limit because your client has taken over the risk of errors in the product specification (i.e. customer risk).

Thirdly, you may want to take a certain calculated commercial risk by having the upper uncertainty limit extended into the fail zone above the upper specification limit, due to commercial reasons such as keeping good relationship with an important customer. You may even choose to report a measurement value that is exactly on the specification limit as conformance. However, by doing so, you are taking a 50% risk to be found err in the issued statement of conformance. Is it worth taking such a risk? Always remember the actual meaning of measurement uncertainty (MU) which is to provide a range of values around the reported number of the test, covering the true value of the test parameter with 95% confidence.

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