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Inferential Statistics

Inferential Statistics

Importance of Inferential Statistics to Probability Sampling and Research Methods

Inferential statistics is a statistical inference from the statistical comprising methods and procedures to deduce properties of a population statistic, from a small part of population. Inferential statistics covers important aspects that also include the probability sampling. In this context, the inferential statistics is the process by which it is deduced that include the properties or characteristics of a population from a representative sample. One of the main aspects of the inference is the estimation of statistical parameters. For case in point, to find the mean, µ, of the heights of all the soldiers of a replacement, a sample and obtain its mean, 0. The sample mean is an estimator of the population mean, µ. If the sampling process is well done that is, the sample is sized and has been selected randomly then the value of µ is unknown then it may be inferred from 0 (Asadoorian and Kantarelis, 2005). Inferential statistics determine and assess the characteristics or attributes of a particular sample to generate generalizations about a specific population. The meaning of statistical information depends on the clarity and precision of the problem and questions addressed within the topic under investigation. The data collected will be analyzed by using the descriptive analysis. In inferential statistics, effect size is determined through three mechanisms which include standardized mean difference, odd ratio, and correlation coefficient.

Inferential Statistics and Probability Sampling

In probability sampling, inferential statistics are important as the probability sampling entails selecting a sample from a population, selection based on the principle of randomization. It is more complex, takes longer and is usually more expensive than the non-probability sampling. However, as the population units are selected at random and it is possible to calculate the probability of inclusion of each unit ...

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