Statistics and Mathematics: Analysis and Interpretation of Data

This assignment solution explores key concepts in statistics and mathematics, addressing questions on statistical literacy, the use of charts and graphs, hypothesis testing, and correlation versus causation. The student discusses why statistics is considered the language of science and the importance of statistical literacy in interpreting data and making informed judgments. The assignment presents the pros and cons of using visual representations like charts and graphs, providing an example of how such visuals can influence understanding. It then delves into the nuances of hypothesis testing, clarifying the difference between failing to reject and accepting the null hypothesis. Furthermore, the solution debates the statement "Correlation means Causation," offering reasoning for its determination. Finally, it outlines the information needed to test the difference in annual bonuses between two companies using a t-test. The student concludes with reflections on the learning experience and advice for future students.

Running head: STATISTICS AND MATHEMATICS
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Statistics and Mathematics
Name:
Institutions name:

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Statistics is the language of science because it tries to explain the natural and social
phenomenon using figures and facts. Statistically literate means being able to understand figures
and facts and make the correct judgment (Keller, 2015). Statistical literacy is important because
statistical information is of great value in our societies as it impact statistical knowledge for
report writing and analysis.
With charts and graphs to represent data, one can quickly conclude and show the trend at
a glance. For example, the line graph can show the trend of rainfall intuitively. However, only
limited continuous data can be represented (Ott & Longnecker, 2015). Scientifically, failing to
reject the null hypothesis is not the same as accepting it. This is because the aim of any
hypothesis test is to find for evidence to reject or to fail to reject if evidence is insufficient (the
probability is plausible).
Correlation doesn’t mean causation. For example, the correlation between two variables,
a change in one variable doesn’t necessarily cause the change to its counterpart (Anderson,
Sweeney, Williams, Camm, & Cochran, 2016). To test the difference in average of two
independent groups, we need to get the sample mean, sample size and sample standard deviation
for each group. The analysis will test the hypothesis
H0: μ1−μ2 ≤ 100 versus H0: μ1−μ2>100
The test used is t-test (population standard deviations are known). If the computed value is
greater than the critical value (at t α=.05, n-2), we reject the null hypothesis (Chatfield, 2018).
Conclusively, the greatest achievement is that I can run different tests/analysis and
actually understand the logic behind the results. The knowledge and materials acquired will be

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used in conducting research and experiments in the future. Advice to next terms’ students is not
to memorize formulas but rather use distributive practice to perfect their studies.

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References
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2016). Statistics for
business & economics. Nelson Education.
Chatfield, C. (2018). Statistics for technology: a course in applied statistics (3rd Edition ed.). New York:
Routledge.
Keller, G. (2015). Statistics for Management and Economics, Abbreviated. Cengage Learning.
Ott, R. L., & Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson
Education.