The Assessment Of Osborne And Waters

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

In the early to late 1940s, audio recordings of three ex-slave men document their struggle and capture the horrors of slavery. Part of the New Deal programs, these recordings were coordinated to archive the testaments of the few surviving victims of slavery. The interviews were conducted in cities based on the sound of automobiles in the background. Preserved by the Library of Congress, the recordings are eerie and broken because of the deterioration of the audio tape itself. Fountain Hughes, Wallace Quarterman and George Johnson all were victims of slavery in the mid-19th century. Very old now their testaments are chilling reminders of the United States past.

Assuming the variables used to test linear regression were continuous, had a linear relationship, had no significant outliers, showed homoscedasticity as well as independence of observations, we tested a series of bivariate regressions to explain whether or not there was a statistically significant portion of variability in the dependent variable from variability in the independent

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram illustrates the normality of the data.

In the video Don't Be Fooled By Bad Statistics posted by Emily Dressler three forms of bad statistics are discussed, poorly collected data, leading questions, and misuse if center. Information collected poorly will lead to misleading results and false conclusions. Dressler uses the example of data collected by researchers pertaining to magazine preference during business hours. The data is skewed because of the time of day the information was gleaned rendered the sample not representative of the entire population. Another form of bad statistics has to do with how the desired information was elicited. Leading questions may result in biased responses. Questions need to be worded carefully so the information collected is not influenced by the interviewer. Finally, the video talks about misuse of center. Data can be misleading if not appropriately analyzed. Outliers, an individual value that falls outside the overall pattern of data can prejudice the conclusion leading to incorrect assumptions. An example might be that of the man who drowned in a pond with an average dept of one inch. The pond was one quarter inch deep everywhere but in the center where there was a ten foot hole.

In his 2013 book, Naked Statistics, Charles Wheelan explains a field that is commonly seen, commonly applied, and commonly misinterpreted: statistics. Though statistical data is ubiquitous in daily life, valid statistical conclusions are not. Wheelan reveals that when data analysis is flawed or incomplete, faulty conclusions abound. Wheelan’s work uncovers statistics’ unscrupulous potential, but also makes a key distinction between deliberate misuse and careless misreading. However, his analysis is less successful in distinguishing common sense from poor judgement, a gap that enables the very statistical issues he describes to perpetuate themselves.

In Fantuzzo, et al. (1991), there appears to be a lack of base line in which to rely upon the facts, due to the exclusion of what one would consider the social norms. Fantuzzo, et al. should have had a baseline in which to rely giving their study more standing.

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

In responding to our study of the influence that statistical significance has on reviewers ' recommendations for the acceptance or rejection of a manuscript for publication (Atkinson, Furlong, & Wampold, 1982), Fagley and McKinney (1983) argue that reviewers were justified in rejecting the bogus study when nonsignificant

(n.d.). Bias in research. The University of Texas Health Science Center at San Antonio. Retrieved from http://familymed.uthscsa.edu/facultydevelopment/ elearning/biasinresearch.htm

Multiple transformation were attempted to normalize the data, however, none were found to be normal as confirmed through Shapiro-Wilk tests. Therefore, the raw data was used for all further analysis. While some response values did appear to be far off the median none were considered to be outliers and no data points were omitted. Considering that the data was not normally distributed, the median and interquartile range (IQR) were used to report measures of center and spread respectably. The median (center) was found to be 94 (%) while the IQR was reported as 9 which indicates how spread out the middle values are, thus describing the

While sociological research is being recorded, a good sense of reliability and validity is essential for one to have. If one does not have the most honest VARIABLE being observed for their research, they might lie about what they have done. Or, the researcher could lie and all the information gathered would be tainted. If one is not directly honest with one's findings, people will not believe them without solid evidence.

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

Initial statistical courses are exciting, as students are taught the foundations of conducting research by learning about inferential statistics. However, important topics such as power, confidence intervals and effect sizes are briefly discussed and quickly forgotten due to the large amount of new information taught in those courses. Over time, students simply learn to rely on p-values, where p < .05, the null hypothesis is rejected and p > .05, the null hypothesis is retained. Statistics are overwhelming and a lot of practice and reading are required to fully understand how it really works. I am a fourth year student and consider myself as having a higher than average knowledge of statistics compared to most fellow students in my year, but I have a lot to learn and am aware that I still do not understand most of it. That is one reason why NHST is surviving, as it is convenient and a strong statistical knowledge is not needed to apply it in research. The point is that appropriate ways to interpret statistics and ethical research practices should be taught as early as possible to avoid the use of common unethical research practices.

A glass of wine a day keeps the doctor away. Is it enough to just say this without any real evidence to support it? Is just wanting it to be true enough to convince you? The answer to these two questions could be the difference between acknowledging accurate information presented in a scientific article and accepting insufficient information presented in a lay article. Lay articles provide brief summaries on the concepts in documented research studies. Scientific articles thoroughly describe and report findings in research studies. In order for either article to be believable, important information should be included. This information includes the population of interest and the sample in the study, the independent and dependent variables and their levels, scale of measurement for each variable, the null and research hypothesis, the test statistic used and its significance level (p value or α value), results of the study, research errors, and the assumptions for the specific test statistic. According to Nolan and Heinzen (2011), authors of Essentials of Statistics for the Behavioral Sciences, assumptions are, “Characteristics that we ideally require the population from which we are sampling to have (scaled DV, randomly selected, normally distributed) so that we can make accurate inferences (Nolan & Heinzen, 154).” Comparing a lay article to a scientific article can aid in one’s perception of which type of article most effectively presents all of the necessary information