Essay On Inferential Statistics

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Statistics, in general, is a mathematical concept related to the analysis and presentation of data. Ideally, statistics are used to interpret data and make informed decisions. Unfortunately, statistics are often used inappropriately or outright incorrectly in an effort to persuade the uninformed. The informed individual approaches statistical claims and figures in an objective but judicious manner. The online statistics education course authored primarily by David M. Lane provides an introduction to statistical reasoning, data collection, analysis, presentation, and testing. The objective of the course is to aid in the development of personal statistical literacy. This paper will shadow the statistics education course in an attempt to comprehend and relate the statistical methods within to the ten principles of quantitative reasoning.

Types of Statistics

Statistical methods can be partitioned into two logical sets, descriptive and inferential statistics. Descriptive statistics 'describe' the population being studied. Unlike inferential statistics, descriptive statistics should not be used to 'infer' about a population outside of the set being directly represented. Inferential statistics, on the other hand, involve the modeling of a population from the analysis of a sample within that population. In effect, inferential statistics is the extrapolation of the analysis of a sample in order to generalize a larger population. In a sentence, descriptive statistics describe the data at hand while inferential statistics extrapolate the data of a set to draw conclusions about its compliment.
Inferential statistics employ multiple methods of sampling including; simple random sampling, random assignment, and stratified sampling. The...

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...pothesis for an experiment or theory is reliant on semantics and thus syntax. The goal of significance testing is to reject the null hypothesis but not to disprove it.

Significance Testing

Significance testing is directly related to probability. Probabilities that reject the null hypothesis generally start at 0.05 and can approach 0 depending on the value that the researchers choose. The significance level (α) is the maximum probability value that rejects the null hypothesis. Statistical significance is the term used when the null hypothesis has been rejected. It is important to note that the use of the word “significant” here does not correspond to the independent variable having a significant effect on the dependent variable. The term “significant” when used in significance testing simply means that there is some measurable effect, not that it is significant.

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