Inferential Statistics in a Quantitative Study

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Inferential Statistics in a Quantitative Study

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Inferential Statistics in a Quantitative Study

Descriptive and inferential in a quantitative study give details differently concerning the nature of the data. They both give very important and powerful information to be relied on whereby the data is descriptive and predictive. Descriptive statistics describes data in a way that is dispersed. Descriptive statistics gives more details about the study. Graphical representation is another method used in descriptive statistics whereby the data is clearly represented for interpretation. This visual presentation helps one to determine the difference and make comparisons quickly between different data sets. On the other hand, inferential statistics tries to infer the data from the samples in order to make conclusions (Fraenkel, Walle & Hyun, 2009).

Inferential statistics picks a sample from the large population and derive data about the whole population from the sample. In qualitative study there is much generalization whereby there are different ways which lead to generalization. In some instances, the population may be fruitful and also possible. There is random sampling in qualitative study which is also applicable in quantitative study. There is argumentive generalization in qualitative study whereby a large sample is obtained. The consumer should have a well understanding of the importance of p-values in quantitative research. P-value is very important in statistics as is constitutes to what is got from the field (Creswell, Fetters & Ivankova, 2007).

In conclusion, the significance of testing statistics is meaningful when p-value is inclusive. P-value is mostly used in post-experimental to understand about the hypothesis. This is useful to the researcher whereby he or she is able to judge about the plausibility of the post-experiment. P-value is the probability which is tested by has got no effect and can also be called the null hypothesis. The customer should have a clear understanding of the meaning of p-value in the research to help him or her understand how close the results are to the observed results (Fraenkel, Walle & Hyun, 2009).

References

Creswell, J. W., Fetters, M. D., & Ivankova, N. V. (2007). Designing a mixed methods study in primary care. The Annals of Family Medicine, 2(1), 7-12.

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to design and evaluate research in education (Vol. 7). New York: McGraw-Hill.




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