Theoretical Statistics

Wienclaw, Ruth A.
April 2018
Theoretical Statistics -- Research Starters Business;4/1/2018, p1
Research Starter
Statistics allow one to organize and interpret data that would otherwise be incomprehensible. However, statistics is much more than a set of mathematical techniques that are used to manipulate data in order to derive an answer. For statistics to be truly useful, one must recognize and understand the fact that there is an underlying uncertainty and variability in data and collections of data. Analyzing and interpreting data using statistics is a messy process and sampling error, measurement error, and estimation error can negatively impact the results. In addition, not every statistical technique is appropriate for use in every situation. The researcher needs to be careful to pick the correct technique to match the characteristics of the data being analyzed. Statistics do not yield exact results, but only probabilities. Although not an exact science - or at least not a science of exact results - if one understands the theoretical underpinnings, statistics can be of immeasurable help in understanding the phenomena of the real world.


Related Articles

  • ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH. OYEKA, I. C. A.; EBUH, G. U.; NWOSU, C. R. // International Journal of Engineering Science & Technology;Feb2012, Vol. 4 Issue 2, p811 

    This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method...

  • Heterogeneity in the Frequency Distribution of Crime Victimization. Hope, Tim; Norris, Paul // Journal of Quantitative Criminology;Dec2013, Vol. 29 Issue 4, p543 

    Objectives: Tests the idea that the frequency distribution typically observed in crosssectional crime victimization data sampled from surveys of general populations is a heterogeneously distributed result of the mixing of two latent processes associated, respectively, with each of the tails of...

  • A practical sampling approach for a Bayesian mixture model with unknown number of components. Liqun Wang; Fu, James C. // Statistical Papers;Oct2007, Vol. 48 Issue 4, p631 

    Recently, mixture distribution becomes more and more popular in many scientific fields. Statistical computation and analysis of mixture models, however, are extremely complex due to the large number of parameters involved. Both EM algorithms for likelihood inference and MCMC procedures for...

  • Die Skoenlusmetode: 'n Kritiese oorsig. SWANEPOEL, J. W. H. // Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie;Sep2008 Supplement, Vol. 27 Issue 3, p23 

    Ever since its introduction, the bootstrap has provided both a powerful set of solutions for practical statisticians, and a rich source of theoretical and methodological solutions for problems in statistics. In this paper, a survey of some recent developments in the non-parametric bootstrap...

  • Estimation of the CDF of a finite population in the presence of acalibration sample. Luo, Ming; Stokes, Lynne; Sager, THomas // Environmental & Ecological Statistics;Sep1998, Vol. 5 Issue 3, p277 

    We compare the performance of a number of estimators of the cumulative distribution function (CDF) for the following scenario: imperfect measurements are taken on an initial sample from afinite population and perfect measurements are obtained on a small calibration subset of the initial sample....

  • Data selection and natural sampling: Probabilities do matter. Oaksford, Mike; Wakefield, Michelle // Memory & Cognition;Jan2003, Vol. 31 Issue 1, p143 

    Probabilistic accounts of Wason's selection task (Oaksford & Chater, 1994, 1996) are controversial, with some researchers falling to replicate the predicted effects of probability manipulations. This paper reports a single experiment in which participants sampled the data naturally—that...

  • Overcoming the Fear of Statistics: Survival Skills for Researchers. Williams, Karen B. // Journal of Dental Hygiene;2015 Supplement1, Vol. 89, p43 

    The article discusses a research on the ways of how to deal with fear of statistics in researchers. It highlights the Consolidated Standards of Reporting Trials (CONSORT) Guidelines and Improved Consort guidelines to supply information about minimally important difference (MID). It also...

  • Co-relation between Static and Dynamic Balance in Healthy Individuals between 18-25 Years using One Leg Stance Test and Multi Directional Reach Test. Purohit, Roma Dilip; Sadhale, Aparna // Indian Journal of Physiotherapy & Occupational Therapy;Apr-Jun2014, Vol. 8 Issue 2, p89 

    No abstract available.

  • A Novel Over-Sampling Method Based on EDAs for Learning from Imbalanced Data Sets. Liu Wei; Zhang Dongmei; Li Yang // Journal of Convergence Information Technology;Nov2011, Vol. 6 Issue 11, p237 

    Traditional classifiers tend to favor majority class (negative class) when dealing with imbalanced data sets. SMOTE (Synthetic Minority Over Sampling Technique) is an effective approach designed for learning form imbalanced data set. However, SMOTE has the drawback of a certain blindness. To...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics