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General Summary
Chapter seven of Gaus text focuses on analyzing populations, sampling distributions, and the sample related to criminal-justice statistics and criminology. From a statistical perspective, a population defines a whole (entire) sphere or universe of places, people, objects, and particular units involved in analyzing a research topic (Gau, 2018). For instance, if an investigator in criminology intends to study military officers behavior in a particular camp, the total number of military personnel in the given location will represent the population under the survey. A sample arises as a component or subset of a given populace (Gau, 2018). Furthermore, according to Gau (2018), a population distribution assumes all the particular populaces characteristics under research. In contrast, a sample distribution highlights the given sample values obtained from the original population (Gau, 2018). There are two dominant distributions; empirical (sample and population distributions) and theoretical distributions (drawn from sampling distributions).
Fundamental Arguments
In Gaus text, the central-limit-theorem (CLT) is grounded on the notion that during the computation of descriptive statistics linked to large samples of an infinite number, a normal distribution emerges from the sampling distribution. The actual (real) populations mean describes the point at which the sampling distributions converge during criminology-based statistical computations (Gau, 2018). The concepts of a statistic and parameters are significant in the research related to criminal justice. A parameter is a figure or number that defines the populace from which individual samples are gathered (Gau, 2018). On the other hand, a sample is a figure that expresses or elucidates a sample that originates from the larger populace or source (Gau, 2018). For instance, while investigating the officers in a military camp, the sample of 10 senior officers may be drawn from the total population size of 100. Sampling errors emerge from the phenomenon that multiple and infinite-based random samples may originate or arise from any given populace.
Critique
I believe that the authors presentation of the subject is compelling. However, the author could have considered presenting multiple examples and impressions of the key concepts such as sampling error, the CLT, and theoretical distributions and demonstrate their applicability in various research methods, including experimental criminology. Gau (2018) should have given sufficient information regarding the initial fundamental concepts application to different U.S. criminal justice research pieces. Furthermore, the inclusion of computerized statistical tools to analyze the population and sample distributions would have been crucial in enhancing learners understanding of the conceptualizations.
Reference
Gau, J. M. (2018). Statistics for criminology and criminal justice. Sage Publications.
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