Statistics: Hypothesis Testing and Confidence Intervals

This statistics assignment provides definitions and explanations of key concepts related to hypothesis testing and confidence intervals. It defines the alternative hypothesis as capturing results expected when a statistically significant real effect is witnessed, contrasting it with the null hypothesis. The assignment also explains the 95% confidence interval, sample estimate, and p-value. Furthermore, it interprets given results using both the p-value approach and the confidence interval approach, concluding whether to reject or accept the null hypothesis based on the level of significance and the inclusion of zero within the confidence interval. The assignment references academic sources to support its explanations and conclusions. Desklib provides more such solved assignments.

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PART 1
1) Alternative Hypothesis – This tends to capture the results which would be expected when a
statistically significant real effect would be witnessed. It is the opposite of a null hypothesis
which usually highlights the situation when there is no real effect of any statistical
significance (Eriksson & Kovalainen, 2015).
2) 95% confidence interval – It is the interval which can be said to contain the underlying
estimate with a probability of 95%. For instance, the 95% confidence interval of population
mean would imply that there is a 95% probability that the population mean would lie in the
computed interval (Flick, 2015).
3) Sample Estimate – A sample estimate refers to the point estimate which captures the
characteristics of the underlying sample. For instance, the estimate of the sample mean would
be given by the population mean. Further, the estimate of the sample standard deviation in
case of a normal distribution is given by population standard deviation divided by the square
root of sample size (Hair, Wolfinbarger, Money, Samouel & Page, 2015).
4) P value – It denotes the probability of coming across the observed results when the
underlying null hypothesis related to the study question is found to be true (Eriksson &
Kovalainen, 2015).
PART 2
In order to interpret the given results, two approaches may be adhered to as explained below.
P value approach
The p value obtained is 0.65 which is greater than the assuming level of significance. As a
result, it would be fair to conclude that the given evidence is not sufficient to reject the null
hypothesis. As a result, the alternative hypothesis cannot be accepted (Flick, 2015).
Confidence Interval Approach
The 9% confidence interval which has been indicated in the given case tends to include 0 as
one of the values which implies that the correlation coefficient can assume a value of zero
and thereby cannot be assumed to be significant. As a result, the null hypothesis would not be

rejected and hence alternative hypothesis would not be accepted (Hair, Wolfinbarger, Money,
Samouel & Page, 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.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015) Essentials of
business research methods. 2nd ed. New York: Routledge.