The document discusses the fundamentals of statistics, including its subdivisions: descriptive and inferential statistics. It emphasizes the significance of hypothesis development and testing in statistical analysis, detailing processes for selecting appropriate statistical tests and evaluating results. Additionally, it concludes by highlighting the skills gained through studying statistics, such as understanding measures of central tendency and the ability to graphically represent data.

1. Lessons Learnt in Statistics Essay
Introduction
Statistics refers to scientific study of analyzing and collecting data,
mostly, in large quantities for purposes of inferring positions in a while
from those in representative sample. It is concerned with the scientific
techniques of organizing, collecting, analyzing and interpreting
information with the purpose of making and describing informed
decisions (Descriptive statistics, n.d). Statistics are subdivided into 2
major subdivisions; descriptive statistics, n.d). Statistics can be divided
into major subdivisions: descriptive statistics (dealing with presentation
of numerical data or facts either in tabular or graphical form) and
inferential statistics (involving techniques that make inferences based
on the entire population on the basis of observations made on samples
collected. The study of statistics is also worth learning, especially for
those aspiring to undertake different kinds of research projects in
future as the acquired level of lessons and knowledge learnt is quite
overwhelming.
Descriptive Statistics
This major describes main features of collecting information
quantitatively, (Mann, 1995) or quantitative description itself. The aim
is to summarize sample data instead of using it to learn about the
population the data sample is assumed to represent. This is an
implication that descriptive statistics is not developed based on the
theory of probability (Dodge, 2003).
Descriptive statistics is a presentation of the learner with measures
which are used to describe given sets of data including the measure of
dispersion and variability of the variables (which includes variance (or
standard deviation), maximum and minimum variables values, kurtosis
and skewness) as well as measure of central tendency (comprised of

2. mean, media and mode) of variables (Descriptive statistics, n.d).
Descriptive statistics is applicable in univariate, statistical (involving
description of distribution of a single variable which includes the central
tendency) and bivariate analysis (Babbie, 2009; Trochim, 2006).
Inferential Statistics
This is applied best in testing of certain hypothesis. It is used in
description of system procedures which are applied in process of
drawing conclusions from datasets that are obtained from systems
affected by random variation. This is inclusive of random sampling,
random experimentation as well as observational errors (Dodge, 2003;
Upton, 2008). Propositions made on population are based on data
obtained from a population of interest through random sampling.
Inferential statistics has 2 major kinds of errors which are the result of
the means that is used in conducting the process. These errors are:
sampling error (random error and chance) and sample bias (constant
error as a result of inadequate design).
Hypothesis Development and Testing
The key of statistics at all times is a hypothesis. So as to prove whether
or not the hypothesis is worth, it is tested at the end of the process.
The process of hypothesis development starts with identification of
research question. A good topic of research is one that changes focus
from that of a general area of interest to a narrow and more defined
issue (Hypothesis Development and Testing, n.d). The question of
research can be formulated based on general issue of interest area, the
issues the researcher wants to explore as well as the importance of
issues.
This is then followed by the hypothesis of the formulation. A hypothesis
is supposed to be understood in terms that are simple, like a statement
showing relationship between 2 variables that the researcher has an
intention of studying. Often, they are thought as predictions that can

3. confirm a given theory if confirmed or proven. After formulation of the
hypothesis, the process of testing starts immediately. This is supposed
to start with identification of dependent and independent variables
that can be used for purposes of testing the hypothesis. The values of
the variable dependent which can be used to test the hypothesis.
Values of the dependent variable are predicted from independent
variable. The independent variable is therefore presumed on the cause
of the study.
While identifying the dependent and independent variables, it is vital to
take into consideration the question of cause. Cause is defined as the
event like change in a variable resulting in occurrence of another event.
The dependent variable is then looked at as a trend of time selecting it
as an indicator. So as to test relationship between the independent and
dependent variable, a researcher is supposed to make a scatter report I
excel and correlation coefficients are recorded.
Selection of an Appropriate Statistical Test
This is a very preeminent process in analysis of research data. Using the
wrong statistical tests can be witness in instances where parametric
statistical tests are used in testing data not in compliance with normal
distribution or paired tests that are used for unpaired data. So as to
select the ideal statistical test, a researcher is supposed to consider the
type of data involved whether it follows normal distribution or not and
lastly, the objectives of the study (Manly, McDonald & Thomas, 1992).
Evaluation of Statistical Results
After collection and analysis of data, it is important to do an evaluation
of the validity of statistical data. Evaluation of statistical results can be
used in analysis and interpretation of categorical or numerical data. To
do this, all relevant data for a given sample and parameter needs to be
pooled. This is an implication that all data obtained should be used in

4. the calculation of statistical parameters. The standard deviation
observed and sample mean are calculated. Evaluation of statistical data
involves identification of P-value of any given data. The value is
responsible for measuring difference between null baseline or
hypothesis and the alternative hypothesis that is being tested. The P-
value makes it possible for the researcher to establish whether null
hypothesis can be valid or not. The researcher has to select appropriate
statistical tool in order to facilitate the process of evaluation. The kind
of statistical tool to be used depends on the kind of statistical data that
needs to be evaluated (Veves, n.d)
Conclusion
Statistics has helped learners to a great length by acquitting them with
basic skills and knowledge which are as highlighted above. In a nutshell,
knowledge acquired in statistics familiarizes the learner with
terminologies used in the field. The learner is able to graphically
represent data and understand the basic distributions of frequency.
Knowledge acquired in inferential and descriptive statistics equips the
learner with professional knowledge on how to measure central
tendency of a given set or group of data or ungrouped data. Lastly, the
learner is able to evaluate skewness and dispersion of a given set or
group if data and/or ungrouped data.
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5. References
Babbie, E.R. (2009). The Practice of Social Research (12th Ed.).
Wadsworth.
Descriptive statistics. (n.d.). 16th
January 2014. Retrieved from
http://www.acad.polyu.edu.hk/~ machanck/lectnotes/c1_des1
Descriptive statistics. (n.d.). 16th
January 2014. Retrieved from
http://www.investopedia.com/
terms/d/descriptive_statistics.asp#axzz2DxCoTnMM
Dodge, Y. (2003). The Oxford Dictionary of Statistical Terms.
Hypothesis development and testing. (n.d.). 16th
January 2014.
Retrieved from
http://www.ssdan.net/kidscount/modules/osborn_hypothesis
Manly, B. F., McDonald, L., & Thomas, D. L. (1992). Resource selection
by animals: statistical design and analysis for field studies. Springer.
Mann, P.S. (1995). Introductory Statistics (2nd Ed.).
Trochim, W.M. K. (2006). “Descriptive statistics”. Research Methods
Knowledge Base. Retrieved 16January 2014.
Upton, G., Cook, I. (2008) Oxford Dictionary of Statistics
Veves, A. (n.d.). Evaluating the Quality of Data Through Statistical
Analysis. 16th
January 2014. Retrieved from
https://www.acfas.org/Physicians/Content.aspx?id=674
Yang, Y. (1999). An evaluation of statistical approaches to text
categorization. Information retrieval, 1(1-2), 69-90.
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