Solving How Statistics Could Be Done Incorrectly

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

|A random group of apartments was selected from a city to analyze the number of bedrooms they have. Is there evidence to reject |

Do Number of Stores Influence Conclusion- This has no impact on conclusion because the data range for stores was from 27 to 87 giving it a mean of 59.8 and SD of 27.5. This demonstrated that the numbers of stores that participated in the programs was sufficient enough to analyze and interpret. Program V just happens to have the highest number of stores participating, but that would be irrelevant if it wasn’t the most profitable.

Whether research is experimental or developmental, there are no guarantees of perfect study processes or results, since both random and systematic errors are expected. Errors and uncertainties of a study’s outcomes surface almost every time. Faulty, aged or incorrectly calibrated instruments, during an experiment, can lead to important alterations of results. Distracting environments definitely influence the outcome. Finally, the human parameter in the sense of having ability to properly operate instruments and correctly interpret measurements definitely consist another factor of imperfect research (Bell 7-9).

To test just exactly how E.S.P. worked, he created an experiment with Zener cards, which had one of a total 5 pictures on each of them. Rhine would draw a card form the deck and ask the subject to guess what picture was on the card. Out of the many subjects tested, most guessed only about 20% of the cards correct, but one young man averaged about 50% correct. This young man, Adam Linzmayer, would even guess up to 9 cards in a row, which was almost a one in a million chance—he did it three times. Rhine became overly excited of his findings on his belief in E.S.P. and wanted to publish the results. But upon his replication of the experiment, Linzmayer’s success rate of guessing the drawn cards greatly decreased (Lehrer, para. 12-13). This decline effect could possible be due to regressive fallacy, which is the inability to account natural and unavoidable fluctuations in experiments. For example, things like stock market prices and chronic back pain unavoidably fluctuate between prosperous and well-feeling times to poor and pain-filled times. By setting aside the idea of natural and unavoidable fluctuations, one can ultimately fall into self-deception and into post hoc

1.     What does p = .05 mean? What are some misconceptions about the meaning of p =.05? Why are they wrong? Should all research adhere to the p = .05 standard for significance? Why or why not?

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

My null hypothesis, which is a statement of no difference, association, or effect and is tested against the alternative hypothesis (Sullivan), is that BMI is not associated with systolic blood pressure. (Ho: BMI is not associated with SYSBP.) The alternative hypothesis or research hypothesis is Ho is false. (H1: Ho is false.) The p-value, or the likelihood of observing the sample data if the null hypothesis is true, is set at 0.05. (p-value (): 0.05)

Statistical significance is when methods used to reach a conclusion that some findings are effective but common sense may find that findings do not make enough difference to justify a use or to be practical.

The customers in this case study have complained that the bottling company provides less than the advertised sixteen ounces of product. They need to determine if there is enough evidence to conclude the soda bottles do not contain sixteen ounces. The sample size of sodas is 30 and has a mean of 14.9. The standard deviation is found to be 0.55. With these calculations and a confidence level of 95%, the confidence interval would be 0.2. There is a 95% certainty that the true population mean falls within the range of 14.7 to 15.1.

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.

As mentioned before, researcher have to accurately report results. In addition, the concept of plagiarism is a critical factor in psychological research. Everything has to be given proper citation, from quotes to tables used.

On the first day of school, Mr. Serwach’s AP Statistics class participated in an anonymous survey. Nine students answered questions ranging from their age to the number of AP classes they have taken to the number of states they have visited. The purpose of the survey was to gather some data about the class so the students could demonstrate their writing and analysis skills. Based on the results, several conclusions can be drawn to describe the population (which is just the 3rd-period statistics class because the results do not apply to anyone other than the students in the class). For example, the class is likely composed of 11th and 12th graders since the ages of the class range from 16 to 17 years old. Another speculation is that the

If the P value is large, the data do not give you any reason to conclude that the population mean differs from the hypothetical value you entered. If the P value is small (usually defined to mean less than 0.05), then it is unlikely that the discrepancy you observed between sample mean and hypothetical mean is due to a coincidence arising from random sampling (Interpreting Results, (n.d)).

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.