Practical Connection

Practical Connection 2

Vindhya Anduri

University Of Cumberland’s

Inferential Statsistics in Decision making (ITS-734-M21)

Prof: Dr. Doug Bennett

Topic: Practical Connection

9/18/2021

Human life is always associated with making General Iinferences and statements assumptions about the human condition which are general with reference being often made from based on specific shreds of evidence and experiences. Application of inferences relies mostly on probability and frequencies of events happening. Proper understanding of inferential statistics can help drive quality improve the decision-making capabilities of an individual. This A proficient working knowledge of inferential statistical applications helps in the proper analysis and interpretation of statistics helps one to properly analyze and interpret data. This article outlines how inferential statistics can be used in decision making applying through the practical application of the related knowledge, skills, and theories learned practically.

The m Main areas concepts of inferential statistics are include estimated parameters, confidence intervals, and the test of hypothesis testing. These concepts are used in the process of making a converting raw data and statistical analysis into actionable information to drive effective decision-making. In the modern-day business environment, effective decision-making stands out to be (Be succinct – avoid wordiness) is crucial for the success of an organization. It Working towards creating a culture of effective and evidence-based decision-making should be a responsibility priority for all organizations to work towards creating a culture of effective and evidence-based decision-making (How so? Elaboration needed here. Tell/show why a positive culture and evidence based decision-making is important). This (Avoid unclear antecedents Use specific language) Evidence-based decision-making has not been the case in practice of many organizations (How so? Citation needed here to support your assertion). Decisions made in an organization should not rely on intuition but rather, on an evidence-based statistical decision-making model. Proper understanding of inferential statistics is needed to improve an individual’s ability to make decisions making, make and informed predictions, and in to conducting research (Pfleger, 2018). Having this knowledge is important because one avoids situations of statistical misuse or misinterpretation in statistics. 

Having gone through the The writer can apply the statistical concepts learned in this course, I (Use third-person voice per APA guidelines) will be of great importance in my working station helping my to help her employer in to makinge informed decisions. I want The writer is working to work with one of the biggest clothing lines in the world to understand how the business part is done giving me enough experience operates to start an enterprise of my her own. I She will use her knowledge in of inferential statistics to collect data samples and make drawn conclusions from inferences about the specific related population. This (Unclear antecedent. What is “This”?) will be a good application of sampling which works well when one does not have the whole picture of a scenario knowledge about the population. I The writer would survey sample groups of people asking the best to learn their preferred products and brands they enjoy buying for the cloth company (Be succinct in APA style – avoid wordiness). After The writer will perform data analysis, I will be in a position to advise the company of the best-selling products to consider investing in them more guide her business investment. Reducing the uncertainty associated with this method, I would The writer will take steps to make sure ensure that the survey sample chosen represents the target population (Brase, C. H., & Brase, 2016). Increasing the effectiveness of the decision made To increase the validity of the data collection, I would the writer will use complex a quasi-experimental designs to gather the most convenient samples (Need to explain here why using a quasi-experimental design will ensure the most convenient samples are assembled). 

Using inferential statistics, the organization (Which organization? Use specific language) will be able to get answers to questions of how many, how much, and how often (Specify how many of what?; how much of what?; and how often what occurs?). Using the Nnull Hhypothesis significance testing will also be used to assist in decision-making. This Significance testing will involve getting provide an estimate of the probability of a sample average being different from the expected value. The expected value here is reflected by the the null hypothesis. Taking an example in the Using a clothing company as an example, one could investigatinge whether two groups are different on not, Next, a null hypothesis would could be stated as test “the difference between teenagers and young adult taste of clothes is in a ratio of 2:3. If the difference was say 5.1, the p-value, in this case, wcould be 0.05 ( p > .05 or p < .05?) (Brase, C. H., & Brase, 2016). This gives a 5% difference in our sample if the true difference stands at the ratio given. 

This paper shows how inferential statistics can be used in making the informed decisions in a practical business environment. Proper application of knowledge and skills learned is also demonstrated. Human beings have a tendency towards inference which forms the basis of inferential statistics (Cite source of this assertion here). This branch of Inferential statistics can helps in to answering critical questions of how much, how many, and how often in most any given business decision. This critical information inferred about the population is important because its a good proper application and proper understanding of the statistical process prevent misinterpretation can provide valuable information critical to the success of business and industry of statistics.

References (References page is a separate page)

Brase, C. H., & Brase, C. P. (2016). Understanding basic statistics (7th ed.). Boston, Massachusetts: Cengage Learning.

Pfleger, I. (2018). Inferential Statistics. In R. Kimmons, The Sstudents' Gguide to Llearning Ddesign and Rresearch. EdTech Books. Retrieved from https://edtechbooks.org/studentguide/inferential_statistics  

Vindhya,

Your paper is focused, provides some support for assertions, notes specific applications of inferential statistics, and shows strong potential. You also provide a specific example of application at your work – good. There are, however, items to understand and address to strengthen your future work. Be sure you understand the feedback items highlighted in your paper and the summative items listed below. Using the feedback on this paper, you may revise and resubmit this paper for additional points. On your revision, be sure to remove the highlighted feedback and colors (green, red, and yellow). Also, make any deletions/additions you find necessary. Highlight the revisions you make in light blue. Doing so will save time on review and grading.

· For APA style writing, use third-person voice (even when you are referring to yourself). Avoid using first- or second- person (“I”, “We”, “You”, “My” etc…)

· Ensure your ideas are clear by using specific language, explanation, and examples.

· Be succinct (avoid wordiness)

· Support all of your assertions with explanation/evidence.

· Avoid using unclear antecedents such as “this” or “it” unless it is clear and specific what they are referencing. Use specific language to ensure clarity.

These items can quickly be addressed with commitment, study, and practice. Let me know if there is any feedback that is unclear, and I will glad to help you.

· Be sure to end paper with a conclusion paragraph that summarizes your key points and reiterates the big idea(s) you wish to communicate.

· Review capitalization rules.

· Speed out the word/phrase the first time it appears in your paper. Father that, you may use the abbreviation by itself.

· Practice syntax/sentence structure to promote clarity.

Listed below is a key to the highlighted feedback.

Highlighted Feedback Key

Red = Items recommended to delete 

Yellow = Recommended revisions 

Green = Guidance/Feedback   

Hang in there!