Examining Statistical Procedures And The Justification Of Knowledge

There has been an ongoing debate on whether psychology is indeed scientific, although recent, psychology is now considered a science. This is because it uses scientific methodology in researching, devising treatments and measuring the outcomes. These methods include collecting and analysing data and concluding their findings in order to identify whether the research or treatment adequality solves the problem. Scientific studies must be replicable, this means the if repeated exactly the same, the results should produce an identical outcome. Replicability can be increased by ruling out any alternatives that may not have originally been thought of. Objectivity in science is the idea that scientist, in an attempt to get the best results, must first

Cohen’s paper The Earth is Round (p>0.05) is a critique of null-hypothesis significance testing (NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests ways in which researchers can improve their research, as well as the way they report their research. Cohen’s main point is that researchers who use NHST often misinterpret the meaning of p-values and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is where the researcher incorrectly accepts the false null

The main purpose of the most researchers in conducting a research study is to achieve a statistically significant result. When we say statistically significant, it means that the result in a research study was not attributed to chance. In addition, it also means

In order to know whether the evidence of research studies are accurate, one must be able to have a fundamental understanding in statistical analyses to determine if such descriptions and findings within manuscripts and articles are presented correctly and explicitly (Sullivan, 2012). Proper use of statistics begins with the understanding of both descriptive and inferential statistics. Correct organization and description of data characteristics from the population sample being studied leads the researcher to identify a hypothesis and formulate inferences about such characteristics. It is with inferential statistics that researchers conduct appropriate tests of significance and determine whether to accept or reject the identified null

Classic studies in psychology are not worthy of the term — all such studies are vastly over-rated. Discuss.

COMMENTS argument is that because the average effect size for published research was equivalent to that of a medium effect, the reviewer 's decision to reject the bogus manuscript under the nonsignificant condition was "reasonable." Further examination of the Haase et al. (1982) article and our own analysis of published research, however, demonstrates that the power of the bogus study was great enough to detect effect sizes that are typical of research published in JCP, which was our intention when we designed the bogus study. First, although the median effect size (if) for all univariate statistical tests, significant and nonsignificant, reported by Haase et al. (1982) was .083, this index was steadily increasing at a rate of approximately .5% per year, so that the projected median if- in 1981 (the year our study was completed) would be .13. Importantly, an r)2 of .13 corresponds to an effect size (/) of .39, which Cohen (1977) designates as a large effect. A further examination of the Haase et al. (1982) data also lends support to our argument. Their analysis examined the strength of association for 11,044 univariate statistical tests derived from only 701 manuscripts; thus, each manuscript reported an average of more than 15 statistical tests. Since statistically significant and

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.

Since statistics are frequently directed to increase credibility of the information, people are often prompted to make a wrong decision based on some false statistics. This is very scary, especially when talking about health statistics and medications. The study of statistics provides us with the necessary tools to be critical, smart and respond judiciously to the evidence delivered to us. The first thing we should always do is question the numbers, find out where the information is coming (whether the source is credible or not), as well as find out what procedures were utilized to gather and process the data.

     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.

Psychological research may be undertaken on a subset of society; however the importance of the results may have an effect way beyond the group tested. The value of psychological research varies and the same results can be used in different ways by different groups of society.

Kirk (1996) had major criticisms of NHST. According to Kirk, the procedure does not tell researchers what they want to know: In scientific inference, what we want to know is the probability that the null hypothesis (H0) is true given that we have obtained a set of data (D); that is, p(H0|D). What null hypothesis significance testing tells us is the probability of obtaining these data or more extreme data if the null hypothesis is true, p(D|H0). (p. 747) Kirk (1996) went on to explain that NHST was a trivial exercise because the null hypothesis is always false, and rejecting it is merely a matter of having enough power. In this study, we investigated how textbooks treated this major problem of NHST. Current best practice in this area is open to debate (e.g., see Harlow, Mulaik, & Steiger, 1997). A number of prominent researchers advocate the use of confidence intervals in place of NHST on grounds that, for the most part, confidence intervals provide more information than a significance test and still include information necessary to determine statistical significance (Cohen, Gliner, Leech, & Morgan 85 1994; Kirk, 1996). For those who advocate the use of NHST, the null hypothesis of no difference (nil hypothesis) should be replaced by a null hypothesis specifying some nonzero value based on previous research (Cohen, 1994; Mulaik, Raju, & Harshman, 1997). Thus, there would be less chance that a trivial difference between intervention and control

The degrees of freedom (df) of an estimate is the number or function of sample size of information on which the estimate is based and are free to vary relating to the sample size (Jackson, 2012; Trochim & Donnelly, 2008).

The p-value obtained from the data collected did not agree with the original hypothesis conjured, as 2.633 x 10-6 is less

Many Empirical Research involve statistical techniques to make reliable interpretation and inference of study results. P values are one of the important techniques used in statistical analysis. Researchers routinely consider a small p value (less than 0.05) to be "significant”, that is, to have a low probability of obtaining a result that is extreme under the null hypothesis. However, a small p value can also be misleading sometimes and can cause severe problems in the interpretation of study results. In this paper, I will provide a summary of p value followed by explaining how (1) a huge sample size, (2) a homogeneous sample, and (3) heteroscedastic error terms in regression models can lead to a small p value and how to make good use of it.

Benjamin JR, L. T. (2006). Hugo Munsterberg’s Attack on the Application of Scientific Psychology. Journal of Applied Psychology, 91(2), 414-425. http://dx.doi.org/10.1037/0021-9010.91.2.414