Inferential statistical analysis

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Conducting inferential statistical analysis

In examining the different inferential statistical methods, consider their fundamental characteristics, underlying assumptions, strengths, weakness and suitability for producing generalisations regarding an unbeknown population.

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Inferential Statistical Analysis

Inferential statistical methods are the critical component of testing and estimating different parameters of statistical hypotheses. Primarily, unlike descriptive statistics that mainly focus on analyzing data and summarizing the result in a meaning way, inferential statistical methods distinguish between a study population and a sample thereby assessing the strength and weakness of the relationship between dependent variables and independent variables (Dien, 2010. p. 142). Based on unknown population or statistical data that do not comply with inferential statistical criteria, different inferential statistical methods enable determination of the strength and weakness of sample relationships. According to Fernández and Hermida (1998), inferential statistical methods enable characteristics probability exploration of the population under study based on sample characteristics.

On a broad front, the fundamental characteristics of inferential statistics rely heavily on the means difference tests (t-tests), analysis of variance and sophisticated statistical model application. However, the primary characteristics of inferential statistics are reflected on the individual type of inferential statistic methods such as multiple-variate regression, bivariate regression, confidential interval, one sample hypothetical test, t-test or ANOVA, Chi-Square Statistic and contingency table as well as the Pearson correlation. Kalbfleisch and Prentice (2011.p.147) states that as ANOVA or t-test provide continuous and categorical variables characteristics of the unknown population data generation, confidence interval provide basic features of estimated scores or values in that population sample.

Comparatively, the underlying assumptions of different inferential statistical methods during the production of data generation in the unknown population are usually based on three different conditions. Before analysis inferential statistics, a complete list of the participants of the unknown population has to be provided for analysis. Besides, assumptions are made that a random sample has been drawing from the unknown population. Similarly, it’s pre-assumed that the sample size of the unknown population is large enough advocated by Box and Tiao (2011.p.167) theoretical arguments.

The suitability of applying different inferential statistical methods in data generation of unknown populations is multiple. Inferential statistical methods, unlike descriptive statistical methods, provide reliable and validity of the unknown population study findings and results since they can determine and demonstrate the strength of the relationships of the study samples by assessing the impacts and outcomes of the study variables (Huitema, 2011.p. 49). Besides, the inferential statistical methods are suitable for determining the unknown population characteristics probabilities, generalizing findings and the results of larger and unknown population and comparing the responses of the unknown population participants (Fernández and Hermida, 1998.p.198).

The strength of applying different inferential statistical methods in data generation of unknown populations is that they can analyze and describe with data transformation provides larger predictions for an unknown set of data population. Besides, inferential statistical methods can provide confident of the predicted outcome within a specified range based on categorical and continuous variables as described by Kalbfleisch and Prentice (2011.p.39). Therefore, more mathematical standardization is applied in inferential methods to provide valid and reliable data for unknown population compared to descriptive statistical methods. The weakness of inferential statistical methods in producing the generalization of an unknown population is that the fundamental conditions and assumptions have to be made for the inferential statistical methods to be applicable (Hayes and Scharkow, 2013.p.63). However, inferential statistical methods needs basic mathematical skills for the method to be utilized effectively since multiple mathematical manipulations may expose the findings to multiple statistical errors (Dien, 2010.p.146).

**Bibliography**

Box, G.E. and Tiao, G.C., 2011. *Bayesian inference in statistical analysis* (Vol. 40). John Wiley & Sons.

Dien, J., 2010. The ERP PCA Toolkit: An open source program for advanced statistical analysis of event-related potential data. *Journal of neuroscience methods*, *187*(1), pp.138-145.

Fernández, J.R. and Hermida, R.C., 1998. Inferential statistical method for analysis of nonsinusoidal hybrid time series with unequidistant observations. *Chronobiology international*, *15*(2), pp.191-204.

Hayes, A.F. and Scharkow, M., 2013. The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis does method really matter?. *Psychological Science*, p.0956797613480187.

Huitema, B., 2011. *The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies* (Vol. 608). John Wiley & Sons.

Kalbfleisch, J.D. and Prentice, R.L., 2011. *The statistical analysis of failure time data* (Vol. 360). John Wiley & Sons.

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