Dr Prasanth V
Editor, JPID
Errors
When we compare study group/s with a control group in a research, there can be ‘errors’. Error is the difference between the ‘fact’ and our ‘finding’. In other words, error is the distortion in ‘population parameter’ when compared to the ‘true value’. Difference between the ‘fact’ and our ‘finding’ can be due to three reasons
Random error
Difference between the ‘fact’ and our ‘finding’ is
purely due to ‘chance’ alone. Random error is non
directional and will average out in repeated samples
(result in some samples will show overestimation and
in some samples show underestimation). Whenever
we study a sample (which we normally do), random
errors will invariably creep in. Random error will
be ‘zero or minimal’ only when we study the entire
population. As sample size increases random error
decreases and hence reliability (precision) of the
research increases.
Reliability (precision) simply means repeatability
of the measurements we make, that is, repeated
researches under the similar conditions give identical
results. Reliability doesn’t guarantee accuracy
(validity) of the measurements though it’s a pre-requirement of validity.
Bias
Any error introduced into the study for which a
cause can be identified is called as bias. Result
of the research will show repeated overestimation
or underestimation indicating a directional and
systematic difference from the true value. Bias is
considered ‘directional’ because the result will not
average out in repeated samples (result in every
sample will be either over or under estimation).
Validity of the study will decrease if the bias is more.
Validity is the ability of the research to measure what
it is intended to measure. A research is valid only if
its result corresponds to the truth.
Bias has nothing to do with the sample size. Strong
study design and adherence to the research protocol
will help to reduce the bias. David L Sacket identified
19 bias and later Bernard Choi extended the list to
65. Even though both words are considered synonyms
there are some non-sampling errors other than
bias. Wrong instrument for measurement, improper
variable definition etc fall in that list.
Type 1 (α) and Type II (β) errors
In αerror, a true null hypothesis will be rejected. It
is also called ‘false positive error’ because we have
proved some association which truly is not there.
Normally in medical/dental research, maximum
permissible level of α error is 5% (chance that
observed difference due to chance/sampling error
is less than 5% OR probability of incorrectly rejecting
the null hypothesis is less than/equal to 5%). αlevel
of significance is set by the researcher before the
statistic is computed. Once the null hypothesis is
rejected, the only error possible is ‘α’.
In βerror, a false null hypothesis will be accepted.
It is also called ‘a false negative error’ because our
research did not identify an association which in fact
is existing. Probability of committing Type II is called
βand is usually kept bellow 20%. If null hypothesis is
not rejected, the only error possible is β. Whenever
null hypothesis not rejected, the researcher should
address level of βerror and power. Power (1-β) is
the ability to reduce βerror and it is the probability
that a false null hypothesis is correctly rejected. Non
rejection of null hypothesis can also be because of
low power of the study.
