Statistics and Psychology

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Source: G. C. Britz, D. W. Emerling, L. B. Hare, R. W. Hoerl, & J. E. Shade. "How to Teach Others to Apply Statistical Thinking." Quality Progress (June 1997): 67--80.

The last few weeks we have been covering descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than population,

     The August 1999 article in the American Psychologist discusses proper statistical methods and how they should be utilized in journal articles. Using some of the guidelines put forth in the article, I will attempt to show the extent to which Bach & Bach (1995) follow these principles.

I already knew that math plays an important role in both experimental and clinical psychology. It is necessary to both those conducting research

An ongoing debate continues in the academic and scientific world of psychology in regards to the measurement of hypotheses, theories, and phenomenon. For the researcher, the argument is worrisome as well as tedious in nature. Most have the desire for the greatest statisticians to arrive at a consensus or standard, and allow the remainder to return to research business as usual. Very few if any researchers enjoy, comprehend, or desire to be knee deep in what a p value really means, other than the significance of the effect is less than .05. A statistically significant result allows for a positive hypothesis and a possible publication. However, an honest interpretation of statistical data would be more apt to produce a flawed literary publication that could be less than accurate. For this reason, psychology implores replication as the gold standard for research results. Reliability and validity are the foundational aspects of psychological science; without replication, there is little evidence to support the construct tested. Statistically speaking, results from research must be available and “empirically evaluated to determine their merit” (Thomas & Hersen, 2011, p. 9). Thus, when new measures of statistical inference are used, the same evaluative process is applied. An example opined from Iverson, Wagenmakers, and Lee (2010) offer a paradoxical example applied to a new statistic that could not stand up to the scientific muster of replication.

Psychological research has many advantages within society, helping us to better understand many different aspects of the world around us; this essay will be looking at three different research methods to ascertain the advantages of using methods within psychology. First of all we will identify which methods we are going to examine then we will assess the advantages of using these methods through the eyes of the relevant psychologist against their individual studies but first let’s distinguish what is meant by the term research method. Methods are used within a psychological study to help determine the hypotheses of the psychologist, or can be

Rosnow and Rosenthal (1989) examine statistical procedures and the justification of knowledge in regards to psychological science. Psychology researchers, similar to researchers in other fields, think in inventive ways, resembling hunches and intuitions. Because of this, often progress is the result of guesses and hunches.

We created a survey using the Qualtrics program to test our hypothesis of whether political knowledge and political engagement translate to youth turnout in post-secondary students. The survey consisted of 36 close-ended questions consisting of multiple choice, dichotomous, likert scaling and semantic scaling options. The survey topics included 1) personal information 2) voting history 3) knowledge of voting information 4) political engagement and activism, and 5) testing political knowledge. We decided to ask close-ended questions because it allowed for easier data collection for quantitative data and we had already collected more rich

Aron, E. Aron, & Coups, 2009). Additionally, when using inferential statistics the inferences about the research study exceeds the numbers collected in the study (A. Aron, E. Aron, & Coups, 2009). Therefore, the inferences communicate exactly how reliable data collected for the research study is. The inferences in inferential statistics also convey how significant the information collected is. The information researchers gather for their study only shows a sampling of the group of participants that the researchers use. This sampling used must vary enough to be representative of the reliability and significance of the research study (“Research Methods,” n.d.). There also must not be much variation in the data (scores) obtained for the study. When using inferential statistics the researcher must prove that the results of the study are not based on chance or the outcome leads to chance (“Research Methods,” n.d.). Ideally, if the result is a probability, the researcher’s observation of the difference would be statistically significant (“Research Methods,” n.d.). The difference would be the difference in the variables used in the study. Researchers must use descriptive statistics to establish a research study that is statistically significant. The use of descriptive statistics provides

Confirm a common assumption about data statistics: data statistics is accurate in calculation but can be misleading in interpretation and decision making.

Psychology undergraduates are required to study research methods within their course. Within this are many different statistical definitions which have been considered the most challenging aspect of the curriculum.

Mathematical psychology is it a real subject? Yes it is I was even a little skeptical of this in the beginning but after even a little research I really became convinced that using mathematics in psychology can improve the understanding of the brain and how it works. Mathematical psychology is when you study behavior through mathematical concepts this will allow for more quantifiable data to check and verify the results. Another way to look at it is trying to use statistics and mathematical formulas to predict behaviors. Scientist and mathematicians use formulas and laws to assist in their studies and this will allow them to justify the information that they receive from the studies that they are conducting.

“McNemar’s expertness in statistical theory and methods, and his contributions to the field of psychological statistics, cover an era of expanding knowledge during which he took every opportunity to remain abreast of developments, to examine them critically, and to propose constructive improvements” (Hilgard, Hastorf, & Sears). Quinn McNemar is a well-known 20th Century statistician who researched probability as related to his true passion, psychology. By delving into the personal life of Quinn McNemar, analyzing his life as a well-respected psychologist, and exploring the fundamental contributions McNemar made in the field of statistics, one can come to understand why his expertness is valued by many in the mathematics world today. To begin, however, one must understand what made McNemar the man he was with the goals he held for himself.

Psychology established into a mathematical discipline through a series of events during history. This establishment led to the development of mathematical psychology; a field encompassing empirical methodology (Benjafield, 2015). Furthermore, through the implementation of math in psychology, findings from previous and current studies of psychology influenced the plethora of knowledge available today—directly impacting society’s understanding and application of psychological phenomena. This is articulated through mathematical ideas originating from the ancient Greeks, which inspired further research in the field – abundantly, throughout the past three centuries (18th to 21st) (Benjafield, 2015). Specifically, ideas from Euclid in ancient Greece inspired Gustav Fechner to develop mathematical concepts in his formation of psychophysics (Zudini, 2011). In the 18th century, arguments regarding the implementation of math in science were becoming a common query. The field of psychometrics began during this period and early psychologists like Ernst Weber began developing relationships between mathematical concepts (Benjafield, 2015). During the 19th century, Gustav Fechner developed his field of psychophysics and inspired several future psychologists to continue his work and develop their own ideas of mathematical psychology (Benjafield, 2015; Robinson, 2010). Developments in this field instigated the notion of using experimental psychology during World War I, and the 1950s-1970s