Statistical Significance

a. For each year, create tables of counts of gender and of nationality. Then create column charts of

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

Research results tell us information about data that has been collected. Within the data results, the author states the results are statistically significant, meaning that there is a relationship within either a positive and negative correlation. The M (Mean) of the data tells the average value of the results. The (SD) Standard Deviation is the variability of a set of data around the mean value in a distribution (Rosnow & Rosenthal, 2013).

As Dred Scott exemplifies, a case does not have to be good in order to be “great”. Some of the most important “great cases” are also some of the most terrible cases. Our panel would thus prefer to call the cases that fit Mr. White’s definition “significant cases” rather than “great cases”, because “great” generally implies a value judgment along with its judgment on significance, and in generalizing the idea of a “significant case”, value judgments are typically not consistent. There are significant cases that are great, and significant cases that are the opposite of great. Dred Scott is a significant case but not a great one.

3. Lancet’s editors should not have publish such a controversial study without further academic experiments and investigations.

1“The Cult of Statistical Significance” was presented at the Joint Statistical Meetings, Washington, DC, August 3rd, 2009, in a contributed session of the Section on Statistical Education. For comments Ziliak thanks many individuals, but especially Sharon Begley, Ronald Gauch, Rebecca Goldin, Danny Kaplan, Jacques Kibambe Ngoie, Sid Schwartz, Tom Siegfried, Arnold Zellner and above all Milo Schield for organizing an eyebrow-raising and standing-room only session.

This one dealt with an HIV test, which is about 99% accurate. Then a person was selected at random for the test and this person was found to have an HIV positive result. Therefore one naturally assumes the person has a 99% chance of having that disease; but this is wrong. If we then take a larger sample size, the number of possible false positives increase and the probability of that person having the disease also decrease. Here it is important to note that the test itself is accurate, but there is other outside relevant information needed to make the best and most accurate conclusion. The test gave a wrong positive, which is both possible and probable, or the person has the disease; which is also possible and

Statistics are used in many different ways in my workplace. The use of statistics is for the improvement of quality care and safety. Statistics are also used to measure employee compliance in regards to hand washing and proper use of policies and procedures. We also use charts and graphs to show infection rates, skin integrity, falls within the facility, budget concerns, and many more. These graphs help hospital personal improve care and safety to provide quality care to all patients. Graphs can also be used to measure patient and employee satisfaction.

In their research, Cohen, et al. (2001) and colleagues suggest that randomized controlled trials conducted in research settings, may not provide the whole picture about the most

The popular press article written by Allie Bidwell accurately summarizes the research study in an easy to read manner; whereas the research article includes scientific language and is hard to comprehend at times. The research article is more appropriate to use as an academic source for information compared to the popular press article. The research article is credited to a research team with significant credentials from Washington University and is peer reviewed by the institutional board at the university. The authors of the article had over forty references, which were footnoted within the text and readily identifiable at the end of the article; this is where the facts came from. The article described the number and characteristics of subjects and described the research method in detail, but they could have been more clear and specific. The popular press article was written by a member of U.S. News and is not peer reviewed. The author does not have any references, but she quotes the study and other sources within her article. Bidwell included the number and age of the subjects; however, she did not go into detail about other characteristics of the subjects. Bidwell does an excellent job of summarizing the major components of the research article; she does not go into complete detail in regards to the measures and variables. It seems Bidwell was able to acquire her facts from the research

Interrogating construct validity, how well the variables in the study were manipulated and measured, is also essential when dealing with causal claims (Morling, 2012). Again, because so little information from the methods section was addressed in Bergland's (2014) article, readers cannot validate the construct validly of the study. It would have been

The authors relied heavily on two studies to create their argument. The first study mentioned was the Pinto et al article. In this study, "Pinto and colleagues (5) assessed the

The given information is related to 30 major League Baseball teams for the 2012 season. Here, the sample size is large (n 230) but the population standard deviation (a) is unknown. So, apply one sample t confidence intervals. Find the 95% confidence interval for the mean number of home runs per team. Using MINITAB, following are the steps to obtain the 95% confidence intervals for the population mean. 1) Import the data into one column named as RUNS, 2) Choose Stat Basic Statistics 21-Sample t... 3) Select the Samples in columns option button, 4) Click in the Samples in columns text box and specify RUNS. 5) Click the Options...button, 6) Enter 95% in the confidence level text box. 7) Click the arrow button at the right of the Alternative drop-down list box and select not equal, 8) Click OK twice. output: One-Sample T Runs Variable 30 164.47 32.97 Mean StDev SE Mean 955 CI Runs 6.02 (152.15, 176.78) From the above output, the 95% confidence interval for the mean number of home runs per team is lies between 152.15 and 176.78

Pollastek et al (2012) fail to give those reading the article the salient information that led the experimenters to make their conclusions. The information missing includes the sample population, and how many participants were assigned to the various groups being tested. The failure to provide this information brings into question whether the conclusions drawn are from smaller sample sizes or varied group sizes. Leaving out these details in conjunction with a lack of any analyzable data causes the audience to accept the conclusions of the study without any data to back it

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