Forensic Psychology Essay Examples & Outline
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Effect size can be described as the size, magnitude and size of an effect. The effect size can be calculated from any number of statistical outputs and consequently expressed as the strength or magnitude of the desired reported relationship. The basic formula when it comes to the calculation of the effect size is to essentially subtract the mean from the control group and proceed to divide the numerator by the standard deviation that is found in the control group. The effect size is often expressed as a decimal number, and it is of the essence to note that although numbers that are greater than 1.0 are possible statistically, they often do not occur.
An effect size that is near to 0.00 essentially means that on average the experimental and control groups basically performed in an identical manner. A negative effect size, on the other hand, means that the control group was able to perform better than the experimental group. In fact, for positive sizes, the larger the number of the effect size the more the effective the experimental treatment. It is of the essence to understand the rule of the thumb which states that an effect size in the 0.2 often indicates a treatment that produces a relatively small effect whereas an effect size that stands at its 0.8 shows an extremely powerful experimental treatment. For this issue, it can be shown that the greater the effect size the scientist desires, therefore, the greater the disparity has to be between the control group and the experimental group.
Statistical significance can be described as a low probability that comes with obtaining at least an extreme result when the null hypothesis is true. It is of significance as it helps researchers to make an informed experimental decision of whether the null hypothesis can be rejected. In any research, there is often the probability that the observed effect might have occurred because of sampling error only.
However, if the probability that exists of obtaining at least an extreme large results; the large difference that exists between two or more samples), given that the hypothesis is true can be said to be less than a popularly pre-determined threshold for example a 6% chance. When this occurs, a research can be able to conclude that the observed effect is a strong reflective of the characteristics of the sampled population as compared to sampling error. In order to determine whether a result is statistically significant, a researcher in many cases needs to calculate the p-value that is the probability of observing the desired effect given that the null hypothesis holds true. Under research terms, the null hypothesis is often rejected if the probability value is less than that of the significance or alpha level. The most commonly used alpha level is 0.05. The alpha level plays a crucial role in that it is the probability of rejecting the null hypothesis that is true.
Therefore, in statistics terms, a statistically significant finding is one that the observed p-value is less than 5% and is often written as p
The statistical significance is related to effect size as it could gauge the probability that exists of getting a result that is extremely large if there was no existence of any underlying effect. In fact, the outcome of any test is often conditional probability of the p-value. If the p-value by a case below the scientifically conventionally accepted threshold for example 0.05 then the result is judged to be statistically significant. It is only when the researcher understands that he, or she is dealing with a large sized effect is when one can interpret the meaning of the experiment. It is extremely crucial to understand the while the size of the effect size must be correlated with its importance; there are certain cases where even small effects that are observed may be judged as being important.
(Mart, 2010) Research often shows that it is of the essence to integrate hypothesis testing model in sexual abuse cases. The researcher should use accepted hypotheses and use the effect size and statistical significance to show whether or not they are true. In fact according to (Mart, 2010), the evaluator should not by any chance favor any of the possibilities. A confirmatory bias should be used; this is when the researcher asks questions in an expected direction. (Wakeﬁeld, 2012) In his study shows the effect of not putting into place considerable statistical significance. He can demonstrate that because of improper statistical analysis, potential invalid inflation of diagnostic categories was made. This was mainly because the statistical significance was deemed important when it was not.
Mart, E. (2010). Common errors in the assessment of allegations of sexual abuse. The Journal of psychiatry and Law, 325-343.
Wakeﬁeld, J. (2012). The DSM-5’s Proposed New Categories of Sexual Disorder The Problem of False Positives in Sexual Diagnosis. Clinical social work, 213-223.