Abstract
This document discusses a series of descriptive statistics questions. These range from the four types of errors related to measurement as well as response and non-response related errors. Finally, the paper makes several statistical calculations in order to establish the efficacy and practicality of descriptive statistics. Specifically, these problems rectify issues of accuracy and estimation.

From the Paper
"The four major sources of measurement error are respondent, situation, measurer, and instrument. Respondent errors might occur through respondent misinterpretation of a given question or, in the case of a written survey or questionnaire, actual response error (Lomax, 2001, pp.29-31). A situational error would occur when, for example, a political survey for a given district was being taken in another district. Measurer originated errors can occur in several ways from construction and design of the actual study to poor selection of participants as well as misinterpretation of study results or actual errors in compilation of results. Instrument, such as surveys or questionnaires, often occur because they question or respond to something other than what is being tested or researched. "

Tags:statistics, descriptive, research

Abstract
This paper demonstrates the measurement of central tendency, dispersion, skewing of data and frequencies to investigate if a team that pays higher salaries is more likely to win. Based on these descriptive statistics, the author observes that several teams in major league baseball are paid less, yet have the same amount of wins as the higher paid teams. The paper concludes that higher salaries do not affect winning. The paper includes a histogram but does not include the source data or calculations.

Table of Contents:
The Process
Central Tendency, Dispersion, and Skewing For Data
Frequencies for Wins and Salaries
Histogram
Conclusion of Research
Conclusion

From the Paper
"To calculate the central tendency of the data the mean is a significant measure. Finding the average revenue for 30 teams requires calculating the smallest sum to the largest sum. After finding the sum dividing by the amount of teams in baseball, which is 30 will provide the mean. Each sample item will affect the mean or the balancing point of the fulcrum. The averages above and below the mean will help in determining the team revenue and the availability of revenue to offer top salaries to the superior players in the league."

Tags:decision-making, average, dispersion, frequency, impact

Abstract
This paper compares and contrasts inferential and descriptive nursing statistics. The paper explains that inferential "infers' information about a larger data set, from the information within a smaller dataset, and a large sampling size is very helpful to project accurately.

From the Paper
"A statistic is any number that describes a group of objects. There are two main categories of statistics, descriptive and inferential. In simplest terms descriptive statistics merely describe the group they belong to. On the other hand inferential statistics are used to extrapolate conclusions about a larger group of information from a subset of data. Descriptive statistics: Descriptive statistics are used because in most cases, it is not possible or efficient to present all data such that a reader could quickly interpret it. The following are two examples of descriptive statistics:..."

Tags:nursing, statistics, inferential, descriptive

Abstract
The paper provides a brief overview of descriptive and inferential statistics and shows how these two types of statistics clearly serve different functions, with the former (descriptive statistics) describing things as they are whilst the latter (inferential statistics) describe things as they may prove to be. The paper eschews examples of either type of statistic in operation.

From the Paper
"The following paper provides a brief overview of descriptive and inferential statistics. These two types of statistics clearly serve different functions, with the former (descriptive statistics) describing things as they are whilst the latter (inferential statistics) describe things as they may prove to be. Descriptive statistics are best defined as statistics which define a phenomenon as it presently exists: it consists of percentages, averages,..."

Tags:descriptive, inferential, definition

Abstract
The paper discusses the qualitative versus quantitative issue in educational research. The paper describes the four main purposes to using statistics in educational research. The paper also looks at four types of descriptive statistics and at inferential statistics. The paper addresses the counterbalancing of data.

From the Paper
"Where educational research is concerned, the qualitative versus quantitative issue likely plagues every study that has ever been done, regardless of whether it is qualitative or quantitative in nature, because there are concerns about how each study was carried out. This is largely due to the fact that there are always various arguments and differing opinions as to which one method is better for which type of study. Since most educational studies focus on the qualitative side of things, they do not address statistical figures as much as they would if they were quantitative. Some will see this as a problem with these types of studies, and will want hard and fast data that they can analyze."

Tags:descriptive, inferential, counterbalancing, data, qualitative, quantitative

Abstract
The paper outlines descriptive statistics and relates that the description of the sample population are as follows-240 women, [Group A] 121 participated in the intervention-[Group B] 64 in the agency based home care program and [Group C] 55 in with no home care. The paper then outlines the inferential statistics and where they came from.

From the Paper
"The description of the sample population are as follows--240 women, [Group A] 121 participated in the intervention--[Group B] 64 in the agency based hoe care program and [Group C] 55 in with no home care. Inferential Statistics: Surgeons agreed to no order home care for Group A. There was no significant difference in demographics. Demographics were assessed, surgical recovery and self-care knowledge was measured, utilization of health services was measured through a telephone interview."

Tags:article, review, summary

Abstract
This paper focuses on statistics by explaining the statistical process and the primary purpose of statistical processes and then outlining the best practices for statistical procedures. The paper also explains the purpose of statistics and how they are used for product research.

Introduction
Statistics and Their Importance to Research Investigation
Correct Statistical Processes
Summary

From the Paper
"Before there can exist any intelligent discussion with respect to the topic of statistics one must understand that a statistical process does not stand alone nor does it function without being a part of a much larger plan, namely, research investigation as a whole. Statistics and their accompanying processes are only one such part of the research plan and, as such, must be viewed in totality of purpose over single identification. Without a formidable research plan a statistical process is without merit and akin to discussing how many angles can be placed on the head of a pin. In general, and from a philosophical perspective, the research plans and statistical analysis, according to Ohlson (1998) "...are not unlike an artist's canvas, as they strive to capture forever the intrinsic and observable subject placed before it" (10)."

Tags:clinical, trial, based, directed, experimental, descriptive, approach, need, measurement, data, analysis

Abstract
This paper explains that psychologists use statistics to make sense of the human behaviors; through observation measurement and statistical inference, researchers are able to take the abstract and make it more understandable. The author points out that, in inferential statistics, researchers use probability to make generalizations about the entire population based on the results from the research sample. The paper relates that statistics can be used to dictate public policy; thereby, it is especially important that mis-measurements do not occur particularly by drawing samples from non-normative or incomplete populations; while seemingly straightforward, statistics require judicious application of ethical behavior. 2 figures. 6 tables.

Table of Contents
History
Descriptive and Inferential Statistics
Samples and Populations
Probability
Data
Measures of Central Tendency
Central Limit Theorem
Hypothesis Testing
Related Samples
Correlation of Pre and Post Test
What Does Is All Mean?

From the Paper
"Some of the earliest work in statistics was done by Sir William Perry in 1532, when he began to record the number of deaths in London on a weekly basis. Later in the 1600, James Bernoilli, a Swiss mathematician, begin using probability to predict outcomes. In the 1700s, it was Thomas Bayer who gave birth to the concept of inferential statistics. The normal distribution was discovered in 1733 by a Huguenot refugee de Moivre as an approximation to the binomial distribution when the number of trials is too large. Today, not only do scientists but also many professions rely on statistics to understand behavior and ideally make predictions about what circumstances relate to or cause these behaviors."

Tags:probability, sample, normative, prediction, population

Abstract
This paper discusses how people, in general, like to get a visual picture of what they are hearing about and how, through the media and constant representation of statistical data as hard fact, numbers can control people's opinions on issues. It shows how one of the largest issues regarding statistics and their appealing nature is the fact that most of us are innumerate. It also shows how, in addition to innumeracy, the public's opinion of ideas often leads to skewed views on issues; statistics can become so alluring to activists that they can say something that will change a large group of people's minds on an issue, and then they will get what they want.

From the Paper
"Although even though some statistics are wrong, people want to believe them so bad that they will ignore all logic just so that they will have a numerical view of the situation. Perhaps the biggest real life example of this is a social statistic that Joel Best-in his book Damned Lies and Statistics-describes as "The worst social statistic ever... Every year since 1950, the number American children gunned down has doubled" (Best 1). To anyone using this statistic to promote gun control, this statistic is gold, and it sounds believable too. But if you analyze it you'll find otherwise."

Tags:data, activists, innumeracy, media, numbers, schools

Abstract
The paper looks at how in the newspaper article "Study Finds Health Care Gaps", descriptive statistics were used to evaluate a study that was done by Families USA, a public interest group that monitors health care issues, about health care in the state of Pennsylvania. The paper discusses how the study that was done by Families USA found that more than a quarter of state residents younger than 65 have lived without health care insurance at some point in the last two years. The paper points out that the descriptive statistics allow us to see that the gap in health insurance coverage is not a limited problem just in that state, but is a bigger problem that can be seen everywhere.

From the Paper
"This report also found that Black and Latino adults were even likelier than whites to lack coverage. Almost half of Latinos (46.5 percent), more than a third of blacks (37.7 percent) and more than three in 10 (30.6 percent) of other ethnic or racial minorities lacked coverage compared to about a quarter of whites (24.6 percent). In addition it was found that more than three quarters (76.5 percent) of the uninsured were in working families. It was also reported that families with incomes less than twice the poverty level were far likelier to be uninsured (48.3 percent) compared to families with income above that threshold (19.5 percent) (Krawczeniuk, 2009)."

Tags:data, insurance, coverage