Statistical Concepts and Their Real-Life Applications

This article discusses statistical concepts such as population and sample, central tendency and variation, underlying distribution, and hypothesis testing and their practical applications in various fields such as industrial purchasing, financial market, and quality control.

STATISTICAL CONCEPTS
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A lot of the concepts taught were quite interesting and may be quite useful in the real life
context. Consider the example of population and sample. With regards to industrial
purchasing where the order quantity is quite huge, it is not possible for the buyer to
individually inspect each of the items. As a result, for every batch, a random sample is taken
and the quality of the sample is ascertained based on which the whole batch would be either
accepted or rejected (Medhi, 2015).
Another useful concept which has been learnt in the class related to central tendency and
variation. These tools play a crucial role with regards to summarising the data. This not only
has significant amount of relevance in research but also in everyday life where the average is
the most often quoted value in order to denote a phenomena. For instance, consider concepts
such as national income, unemployment which tend to vary drastically in any nation but on a
country level an average number is worked out which is quoted as being representative for
the country as a whole (Eriksson & Kovalainen, 2015).
This concept has vast applications in the financial market where the performance of stocks
and other financial assets is measured using risk and return which are represented by
underlying variation and average return. Based on the empirical performance of the given
financial asset class, the average returns can be determined using the mean or median value
as may be considered suitable based on the given distribution. Additionally, the risk of the
underlying financial asset is captured through the standard deviation observed in the stock
returns. This enables the investors to make prudent investment decisions with regards to
maximising returns per unit risk (Flick, 2015).
The underlying distribution of the estimate is also imperative as based on the underlying
distributions, the properties would change. One of the most common distributions is normal
distribution which in the contest of financial market can be used for estimating the probability
of earning a given return on an asset with the help of the mean and standard deviation values
(Hastie, Tibshirani & Friedman, 2016). This in turn can drive decision making on the part of
various market participants and also aid in formation of portfolio. The underlying distribution
is also useful in case of quality control where the estimation can be made about the
percentage rejections based on the mean and standard deviation of the parameter of interest
(Lind, Marchal & Wathen, 2016).
Another useful concept which has been learnt is hypothesis testing. This is a tool which finds
practical application in various fields such as purchasing, quality check along with research.

This is because using hypothesis testing the key parameters can be checked using the sample
data. Hypothesis testing is required as the sample statistics are not always the same as
parameter and thereby based on the sample statistics, the parameter value estimates need to
be drawn which can then be used as crucial decision making tools (Flick, 2015).

References
Eriksson, P. & Kovalainen, A. (2015). Quantitative methods in business research (3rd ed.).
London: Sage Publications.
Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research
project (4th ed.). New York: Sage Publications.
Lind, A.D., Marchal, G.W. & Wathen, A.S. (2016). Statistical Techniques in Business and
Economics (15th ed.). New York : McGraw-Hill/Irwin.
Medhi, J. (2015). Statistical Methods: An Introductory Text (4th ed.). Sydney: New Age
International.
Hastie, T., Tibshirani, R. & Friedman, J. (2016). The Elements of Statistical Learning (4th
ed.). New York: Springer Publications.