Statistics and Psychology

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Statistics and Psychology Paper There are numerous applications of statistical reasoning and research methods in the field of psychology. From simple aspects of reading and interpreting psychology articles, to completing personal research, statistics is a necessary concept to understand. The scientific method is essential to research, and many of the concepts cross the lines into statistics. It is also imperative for us to compare and contrast the characteristics of primary and secondary data. Ultimately, the focus of these topics centers on the application of statistical reasoning in psychology.
Statistics in Psychology One might ask themselves how mathematical concepts could possibly apply to psychology. The answer is
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Secondary data is published data, or data collected by others (Triola, 2010). Primary data is preferred in research because the knowledge is obtained first-hand, where secondary relies on the observations of others. For instance, more accurate results in a weight study will come from the direct weighing of the patients than asking them their weight.
Statistics in Research Psychologists use univariate principles when they measure only one variable and multivariate procedure when using variables to ascertain relationships (Chow, 2002). Psychologists often use statistics to identify areas of research interest. In testing a hypothesis, many researchers need to turn questions into testable numerical data. One of the most common statistics applications is the testing of the null hypothesis. The null hypothesis involves the original claim –like 50 out of 100 patients see success in regression techniques to overcome phobias- and turning it into a mathematical claim (µ = 50). The alternative hypothesis represents the difference of a claim, or the probability that it is untrue because the test statistic is outside the given range (µ ≠ 50). These claims are tested, and if it is found that less than 50 patients saw success with regression techniques, then researchers are able to use statistical reasoning to disprove the statement. Overall, statistical reasoning is extremely important in the interpretation of research results
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