1. Running head: STATISTICAL REASONING IN PSYCHOLOGY
Statistical Reasoning in Psychology
Jody Marvin
PSY 315
March 19, 2012
Kelly Davis, Ph.D.
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Statistical Reasoning in Psychology
The scientific method is the approach used by scientists and psychologists
systematically acquiring knowledge and understanding behavior and other phenomena
of interest. Relying on contradictory, subjective, common sense to understand
psychology you would have considerable difficulty establishing an acceptable theory.
Psychologists meet the challenge of posing appropriate questions and properly
answering them by relying on the scientific method and recently published credible
primary data. Evaluating available data and research based on theory and evidence
emphasizes critical thinking while referring to the results of well-designed experiments.
Despite the movement discouraging psychologists from conducting significant tests,
examining the role of statistics in research defends the ability to establish and continue
empirical research. At present, sampling distribution is the bridge between descriptive
and inferential statistics and the probability foundation of significant tests (Aron, Aron, &
Coups, 2009, p. xx).
Define Research and the Scientific Method
Good research uses a theoretical framework, standardized procedures, is
generalizable, and uses objective measurements. The scientific method is a
standardized way of making observations, gathering data, and identifying questions of
interest, formulating a tentative explanation (hypothesis), carrying out testing
(experiments), and interpreting results. The goal is a simple scientific explanation
(possibly a theory) to describe and measure behavior reproduced. Although theories are
abstract statements of relationships, they are designed to describe behavior in a very
broad manner (Kowalski & Westen, 2009).

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The theory helps generate a hypothesis, or tentative belief about the relationship
between two or more variables. Research continues because of inductive reasoning,
including reflection on previous knowledge of facts, and general principles. Deductive
reasoning leads from general to a specific cause and effect. In addition, research is
cumulative and progressive. Scientists build on the work of previous researchers
although an important part of any good research is the systematic inquiry aimed at the
discovery of new knowledge (Kowalski & Westen, 2009).
In psychology, characteristics of ideal research include identical participants in both
the experimental group and control group with both groups exposed to identical testing
situations (except for manipulation of the independent variable). In addition, perfect
representatives within the population receive accurate and appropriate measurement of
the dependent variable for what it is measuring. For example, a variable is a
phenomenon that varies in individuals or changes across circumstances. Experimental
manipulation of an independent variable (X) causes a change in the score of a
dependent variable (Y) because of the actions from the participants or subjects (Aron,
Aron, & Coups, 2009, p. 5).
Random assignment to condition resolves the main issue of equivalence within
participants in both groups. This results in distribution of characteristics distributed
evenly across the different conditions. However, problems arise because of the
Hawthorne Effect, which is the influence of attention and reaction received by a
participant with prior knowledge of assignment. In addition, the placebo effect is the
influence on performance from the expectation or motivation of possible benefits. At the

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same time, a recorded instruction minimizes researcher’s inherent bias (Aron, Aron, &
Coups, 2009, Web 1, p. 4)
An experimenter’s dilemma is to choose whether to place more emphasis on internal
validity (conclusions drawn from the results) or external validity (appropriateness and
breadth). The ideal study includes identical testing conditions in an isolated location to
minimize external influence by standardizing the situation while reducing interruptions.
Reflecting potential limitations, results may not generalize outside the laboratory
whereas complex phenomena may lack control in the lab. Validity of measure
accurately samples the construct it intends to measure. Subsequently the variable
predicts intended responses and behavior with reliability that produces similar scores
with repeated trials (Aron, Aron, & Coups, 2009, Web 1, p. 7).
Primary and Secondary Data
Each research project needs information, or data, to support a hypothesis or help to
understand and answer questions. Primary source data is original data or immediate
data is original, such as a firsthand account of an experiment or research. The nature
of the source is a clue regarding a source or data whether primary or secondary. A
textbook is usually a secondary source that offers other scholars’ interpretations of
evidence whereas a primary source provides the evidence that answers a question.
Primary source data, not interpreted by anyone other than its author or creator, includes
eyewitness accounts whereas secondary data is retrieved by modern scholars or
commentators and possibly published subjectively (Raianski, 2003).
Error within the organization that gathers data reduce reliability of secondary data.
The reliability of data is a function of the organization that gathers, organizes, records,

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and publishes secondary data. Concurrently, a definite time lag exists between the time
the primary data transpires and the time it is made available. Clerical error, error due to
change in collection procedure, and error due to corrected data alter the reliability of
secondary data. Along with credentials and credibility of either a primary data resource
or a secondary data source, always check the newest versions of the data set
(Raianski, 2003).
The Role of Statistics
Statistics is a discipline concerned with the method of pursuing the truth, evaluating the
reliability of data, thinking about outcomes, underlying principles, and logic.
Organization, analysis, and interpretation of a group of numbers are the branch of
mathematics that Statistics focus. The goal is to solve a problem with analysis of
numerical or nominal data. You can enter data related, and see what the results are if
you change the data. You can create charts and graphs, calculate, and present
differences and similarities to illustrate the scientific data graphically and over time. In
contrast, you can scientifically show the parts of the data fitting together. “Statistics use
its own set of tools such as t–distribution, and p–value the probability associated with
the truth of a hypothesis,” (Maillardet, 2009, para. 12).
Achieved through random assignment to condition and Laws of Probability, basic
logic behind analysis infers the independent variable causes the change. Categorical
variables (nominal) involve groups or classifications whereas continuous variables
(numerical) involve an infinite amount of values such as age, height, or blood alcohol
levels. Statistical significance is a mathematical measure of the validity of an
experiment showing the results are either within a narrow margin of the control (base

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values) or are all over the chart. Because one definite conclusion is impossible from the
data, recording a possible error, again, emphasizes studies involving humans are never
100% (Aron, Aron, & Coups, 2009, Web 1, p. 6).

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References
Aron, A., Aron, N., & Coups, E. (2009).Statistics for Psychology (5th Ed.). Upper Saddle River,
NJ: Pearson, Prentice Hall, Inc.
Kowalski, R., & Westen, D. (2009). Psychology (6th Ed.).Hoboken, NJ: Wiley & Sons, Inc.
Raianski, J. (2003). Primary and secondary data.Appraisal Journal, 71(1), 43,13. Retrieved from
http://www.appraisal