What Does It Mean To Have Statistical Significance?

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).

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

The video begins with Peter Donnelly, a statistician bringing up a coin toss thought experiment. The audience was to guess how often the pattern of coin flip would land Head-Tail-Tail compared to the other pattern of Head-Tail-Head. The three options were: "A" the number of coin tosses for the pattern HTH will be greater compared to HTT. "B" both patters would take on average the same time to repeat themselves. "C" the number of coin tosses for the pattern HTT will be greater compared to HTT. I chose option "B" as my answer because every coin flip is a 50/50 chance of getting either head or tail so on average both patters should repeat them equally. I was surprised to find that most people also vote option "B", but in fact the odds favor option "A". On average, it will take relatively less tosses to get the pattern HTT than HTH, about 8:10 tosses. This is because the pattern HTT has a larger chance for success because a third of the pattern is always established while the pattern head tail head appears in clumps.

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Since the p-value of 0.6943 is greater than 0.05, the data is said to be statistically significant. This means the null hypothesis is not rejected and the observed values meet the expected values.

According to Schutt (2008), sampling is defined as a subset of population used in a study to be a representation of the population as a whole. My final project is a pre-hire assessment which analyzes potential risky pattern behaviors and emotions in the work place. One of the most important considerations related to sampling that will need to be addressed in my final project is defining the population that will be taking the assessment.

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?

Random Sample: A sample in which every “person of interest” has an equal chance of being selected into your research study.

21. What is sampling error? Could the value of the sampling error be zero? If it were zero, what would this mean?

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.

Quality Associates, Inc., a consulting firm, advises its clients about sampling and statistical procedures that can be used to control their manufacturing processes. IN one particular application, a client game quality associates a sample of 800 observations taken during a time in which that client's process was operating satisfactorily. The sample standard deviation for there data was .21 ; hence, with so much data, the population standard deviation was assumed to be .21. Quality associates then suggested that random samples of size 30 be taken periodically to monitor the process on an ongoing basis. BY analyzing the new samples, the client could quickly learn whether the process was operating satisfactorily. when the process was not

Let’s suppose you have completed a statistical analysis. The null/research hypotheses are listed below, along with the p-value that you obtained from your testing. Explain whether you have significant evidence to “reject the null” or

added to the limitations of the method. It could be argued that random sampling would provide a

This ‘random sampling error’ indicated that there was no cross section of the target group (generation Y) and in turn was a sample selection error. There were 3 respondents whose results were not analyzed, as they did not fall into the target group of generation Y and this was an administrative error. This is another common research problem is survey non-response. Marketers can unintentionally design surveys which many customers choose not to respond to.

This test was used in order to determine if there were too many or too few runs in a series of data. After conducting the runs test it produced a z-value of -5.9123, which indicates the amount of standard errors of the identified number of runs below the expected number of runs. The p-value indicates how extreme the z value is and with a p-value (0.0001) which is less than .05 or .1 the null hypothesis of randomness is rejected (Figure 1).