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24 SCIENTIFIC INVESTIGATION

place more faith and credence in these findings and conclusion if similar find-
ings emerge on the basis of data collected by other organizations employing the
same methods. To put it differently, the results of the tests of hypotheses should
be supported again and yet again when the same type of research is repeated in
other similar circumstances. To the extent that this does happen (i.e., the results
are replicated or repeated), we will gain confidence in the scientific nature of our
research. In other words, our hypotheses would not have been supported merely
by chance, but are reflective of the true state of affairs in the population. Replic-
ability is thus another hallmark of scientific research.

Precision and Confidence
In management research, we seldom have the luxury of being able to draw
“definitive” conclusions on the basis of the results of data analysis. This is
because we are unable to study the universe of items, events, or population we
are interested in, and have to base our findings on a sample that we draw from
the universe. In all probability, the sample in question may not reflect the exact
characteristics of the phenomenon we try to study (these difficulties are dis-
cussed in greater detail in a later chapter). Measurement errors and other prob-
lems are also bound to introduce an element of bias or error in our findings.
However, we would like to design the research in a manner that ensures that our
findings are as close to reality (i.e., the true state of affairs in the universe) as
possible, so that we can place reliance or confidence in the results.
Precision refers to the closeness of the findings to “reality” based on a sam-
ple. In other words, precision reflects the degree of accuracy or exactitude of the
results on the basis of the sample, to what really exists in the universe. For exam-
ple, if I estimated the number of production days lost during the year due to
absenteeism at between 30 and 40, as against the actual of 35, the precision of
my estimation compares more favorably than if I had indicated that the loss of
production days was somewhere between 20 and 50. You may recall the term
confidence interval in statistics, which is what is referred to here as precision.
Confidence refers to the probability that our estimations are correct. That is,
it is not merely enough to be precise, but it is also important that we can confi-
dently claim that 95% of the time our results would be true and there is only a
5% chance of our being wrong. This is also known as confidence level.
The narrower the limits within which we can estimate the range of our pre-
dictions (i.e., the more precise our findings) and the greater the confidence we
have in our research results, the more useful and scientific the findings become.
In social science research, a 95% confidence level—which implies that there is
only a 5% probability that the findings may not be correct—is accepted as con-
ventional, and is usually referred to as a significance level of .05 (p = .05). Thus,
precision and confidence are important aspects of research, which are attained
through appropriate scientific sampling design. The greater the precision and
confidence we aim at in our research, the more scientific is the investigation and
the more useful are the results. Both precision and confidence are discussed in
detail in Chapter 11 on Sampling.