Data Analysis: Descriptive and Inferential Statistics

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1. Define the difference between descriptive and inferential statistics and provide an example of each.
2.Discuss the descriptive statistics in the Genc & Saritas (2020) article.
-What do you learn from them?
-What type of inferential statistics are applied?
-How do they increase your confidence in the results?
Research Paper
The effects of watching comedy videos on anxiety and vital signs in
surgical oncology patients
Hasan Gen¸ca, Serdar Saritasb,*
a Department of Nursing, Dicle University, School of Health, Diyarbakir, Turkey,
b Department of Surgical Nursing, Inonu University, Fac.of Nursing Malatya, Malatya, Turkey

 

 

Sample Solution

Statistics is at the heart of data analytics. It is the branch of mathematics that helps us spot trends and patterns in the bulk of numerical data. Statistical techniques can be categorized as Descriptive statistics and Inferential statistics. Inferential statistics helps to compare data, make hypotheses and predictions. Descriptive statistics explains already known data related to a particular sample or population of a small size. Inferential statistics, however, aims to draw inferences or conclusions about a whole population. In the Genc & Saritas (2020) article, descriptive statistics were used to describe the sample characteristics of surgical oncology patients, such as age, gender

regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating to take note of that while there is a limited ability to recall lumps of data, how much pieces in every one of those lumps can change broadly (Miller, 1956). Anyway it’s anything but a straightforward instance of having the memorable option huge pieces right away, somewhat that as each piece turns out to be more natural, it very well may be acclimatized into a lump, which is then recollected itself. Recoding is the interaction by which individual pieces are ‘recoded’ and allocated to lumps. Consequently the ends that can be drawn from Miller’s unique work is that, while there is an acknowledged breaking point to the quantity of pi

regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating to take note of that while there is a limited ability to recall lumps of data, how much pieces in every one of those lumps can change broadly (Miller, 1956). Anyway it’s anything but a straightforward instance of having the memorable option huge pieces right away, somewhat that as each piece turns out to be more natural, it very well may be acclimatized into a lump, which is then recollected itself. Recoding is the interaction by which individual pieces are ‘recoded’ and allocated to lumps. Consequently the ends that can be drawn from Miller’s unique work is that, while there is an acknowledged breaking point to the quantity of pi

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