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How should data be analyzed and patterns identified? Are the bulk of the data of greatest interest or are the fewer, rarer, possibly outlying, values most informative in answering relevant questions? How should outliers be handled? How well suited for handling small data sets are statistical hypothesis tests? These are questions that robust statistical theorists attempt to answer.
This paper introduces two basic concepts in statistics: (i) descriptive statistics and (ii) inferential statistics. Descriptive statistics is the statistical description of the data set. Common description include: mean, median, mode, variance, and standard deviation. Inferential statistics is the drawing of inferences or conclusion based on a set of observations. These observations had been described by the descriptive statistics. From these descriptive statistics, an inference is made subject to a predefined limit or error or confidence interval. The error in concluding the inference is called inferential error. There are two types of inferential errors: (i) Type I error and (ii) Type II error. Type I error occurs when the researcher accepts the alternative hypothesis despite contrary evidence. Type II evidence occurs when the researcher rejects the alternative hypothesis despite supporting evidence.
Research on Cognition Disorders, 2020
Learn the concepts of elementary and advanced Statistics. Understand the mathematical operations so that you can function as a scientist without depending on formulas. This book is directed to students and researchers and scholars in Philosophy of Science.
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