ABSTRACT
The article gives a critique of parametric and nonparametric tests and
processes of inferential statistics in
forecasting customer flows in 7 selected
small business enterprises in Uganda. Forecasting is one of the decision making tools in a business enterprise.
This may include forecasting customer flows, volumes of sales and many others. This is a vital component of small businesses success. In the long run, what drives business success is the quality of
decisions and their implementation. Decisions based on a foundation of
knowledge and sound reasoning can lead the company into long-term prosperity;
conversely, decisions made on the basis of flawed logic, emotionalism, or incomplete information can quickly put a
business out of commission. In many instances, business decisions have been
guided by parametric tests and processes and /or non-parametric tests and
processes of inferential statistics, which have subsequently affected the
futures of business differently. As we
refer to population mean knowledge for
hypothesis testing using parametric tests, we only refer to mediums for samples, for nonparametric tests. A
parameter is a characteristic that describes a population. These may include μ
(the Mean), δ2 (the variance) of a distribution. We commonly refer
to the normal distribution, when it is symmetric, with the measures of central
tendency (Mean = medium = mode). Usually these parameters are very useful, when
testing hypotheses to enable researchers and decision
makers infer about the population
using samples. It would always be better to have knowledge of or/and about the
population parameters, but more often than not, we find ourselves with very
minimal, or no knowledge about the population parameters. To make the generalization about the population
from the sample, statistical tests are used. In other words, we want to know if
we have relationships, associations, or differences within our data and whether
statistical significance exists. Inferential statistics help us make these determinations and allow us to generalize the
results to a larger population. We employ parametric and nonparametric
statistics to show basic inferential statistics by examining the associations
among variables and tests of differences between groups. It is recommended by
many scholars that business analysis uses parametric and nonparametric inferential statistics in making decisions
about effects of independent variables on
dependent variables. On the contrary, it is argued that the use of inferential
statistics adds nothing to the complex and admittedly subjective, no statistical methods that are often employed in applied business decision making analysis. There are several
attacks made on inferential statistics, perhaps with increasing frequency, by
those who are not business analysts. These attackers are not in for the use of
inferential statistics in research and business decision making, and commonly
recommend the use of interval estimation or the method of confidence intervals.
However, interval estimation is shown to be contrary to the fundamental
assumption of business decision making analysis.
Share and Cite:
Mukasa, E. , Christospher, W. , Ivan, B. and Kizito, M. (2021) The Effects of Parametric, Non-Parametric Tests and Processes in Inferential Statistics for Business Decision Making
—A Case of 7 Selected Small Business Enterprises in Uganda.
Open Journal of Business and Management,
9, 1510-1526. doi:
10.4236/ojbm.2021.93081.