Question

Using Vetter’s “Descriptive Statistics” article (2017), please complete the following short answer questions.

14. In your own words, what are descriptive statistics? a.

15. What are the basic questions descriptive statistics answer? a.

16. What are the measures of central tendency? a.

17. What is the definition of a confidence interval (CI)? a.

18. “The __ the sample size, the more ___ the confidence interval (CI).” a.

19. What are three tools often used by researches to make inferences and generalized conclusions from their data? a.

20. What are some common ways to graphically represent data? a.

Answer 1

Q14]

DESCRIPTIVE STATISTICS

Descriptive statistics refers to the specific methods used to calculate, describe, and summarize the assimilated research data in a logical, meaningful, and efficient manner.

Descriptive statistics help to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. They provide simple summaries about the sample and the measures.

Descriptive statistics are reported numerically in the manuscript text and/or in its tables, or graphically in its figures.

The basic requirement of descriptive reporting is the presence of a clear, specific, and measurable definition of the disease or condition in question.

Q15]

A good descriptive reporting provides answers the five basic W questions namely :

Who, What, Why, When, Where and so what?

Q16]
The three measures of central tendency of a set of data are mean, median, and mode  

When a set of data is truly normally distributed, its distribution forms or follows a bell-shaped curve (Gaussian distribution), and its mean, median, and mode are all at the same point, at the center of the distribution.However, such a distribution is an ideal thing, and is seldom the case ; instead these three values differ from each other..

1) Mean

The mean is the most widely known descriptive statistic.

In order to compute the mean, all the values are added up, and the sum is divided by the number of values.

For example, the mean or average quiz score is computed by adding up all the scores, and dividing the sum by the number of students taking the exam.

The symbol for the mean of a sample is x̅ .

2) Median

The median is a middle number or a value in a sample or population. It is the number above and below which there are an equal numbers of data points.

For example, it is the midpoint in a set of test scores, which marks the 50th percentile of their data distribution. When there is an even number of values, the median is the average of the two middle values.

Median is denoted as P50 or Mdn.The median is typically reported for ordinal data or continuous data that do not have a normal (Gaussian) distribution.

3) Mode

The mode is the discrete number or integer that occurs most commonly or frequently in the data set. Most data distributions have only one mode and are thus termed as unimodal.

If a data distribution has two modes (two “frequency peaks”), it is termed bimodal. There is no symbol for the mode.

Q17]

In an observational study, the Confidence interval (CI) is the range of values within which the true strength of the association between the exposure and the outcome in the population likely resides.

The CI can be formally defined statistically as follows:

“If the level of confidence is set at 95%, it means that if data collection and analysis could be replicated many times, the Confidence interval should include within it the correct value of the measure, 95% of the time.”

Q18] “The larger the sample size, the more narrow and precise the confidence interval (CI).”

There is an inverse relationship between width of the Confidence interval and the sample size;that is, the larger the relative sample size, the narrower and more precise the Confidence interval.

Therefore, the basic intention of conducting a systematic review and then combining (“pooling”) the identified individual study data by a meta-analysis is to create a larger sample size and to generate a more precise pooled estimate of the treatment effect.

Q19]

The three tools commonly used by researchers to generate inferences and more generalized conclusions from the collected data and descriptive statistics are as follows :

i) Testing for statistical significance

ii) Calculating the observed treatment effect size (or the strength of the association between an exposure and an outcome), and

iii) Generating a corresponding Confidence interval (CI).

Q20]

The examples of common ways used to represent the data graphically are :

1. Stem-and-leaf plot
2. Histogram
3. Bar chart
4. Pie graph
5. Line graph
6. Scatter plot
7. Box-and-whisker plot .