• Define descriptive and inference statistic. What is/are the differences?
Descriptive statistics refers to the collection, presentation, description, analysis and interpretation of a collection of data, essentially is to summarize these with one or two pieces of information (descriptive measures) that characterize all of them. The descriptive statistics is the method of obtaining a data set conclusions about themselves and do not exceed the knowledge provided by them. It can also be used to summarize or describe any outfit whether it is a population or a sample, as in the preliminary stage of statistical inference the elements of a sample known.
Statistical Inference refers to the process of making generalizations about the properties of the whole population, based on the specific, which shows their implicit a number of risks. To these generalizations are valid sample must be representative of the population and the quality of information should be controlled , as well as the conclusions and lessons I are subject to errors, you need to specify the risk or probability that one can commit those mistakes. Inferential statistics is the set of techniques used to draw conclusions that go beyond the limits of the knowledge provided by the data, looking for information of a collective through a methodical process of managing sample data.
DESCRIPTIVE STATISTICS:
It is the branch of statistics that deals with the collection, presentation, description, analysis and interpretation of a dataset.
Analyzes studies and describes the unique characteristics of all the individuals in a group.
Its purpose is to obtain information, analyze it, work it and simplify it necessary to interpret quickly.
Has an inductive function.
There are two ways to so...
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...ation. Examples of hospital units would conglomerates, polling, etc.
• Define questionnaire and explain the four questionnaire errors.
Questionnaire is a written genre that aims to gather information through a series of questions on a given topic to finally give overall scores on it. So that, we can say that it is a research tool used to collect, quantify and eventually compare the information collected.
Four questionnaire errors:
1 - The options are not mutually exclusive, which confuses and generates many responses such as "not responding".
2 - The options are ambiguous, causing confusion or answers like "it depends", so the data is lost and there is a high percentage of non-response.
3 - The question makes a false or hypocritical response, eg if improperly closed.
4 - The question refers to different things at the same time question several things at once.
Furthermore, the methods applied convey “the techniques or procedures used to gather and analyze data that is
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
For people who are not statisticians, they may wonder what statisticians do, and how statistics could be applied in daily life. Statistics: A Guide to the Unknown is a supplementary reading materials designed for general readers even if he or she did not learn enough knowledge of statistics, mathematics and probability. Besides, it could give statisticians a general understanding of the important role of statistics in society. This book also analyzes how statistics assists people to gain useful information from massive data sets. In order to form a more respected book, the editors invite many distinguished researchers in statistics as authors. The book consists of twenty-five essays from different fields, including public policy and social science, science and technology, biology and medicine, business and industry, and hobbies and recreation. Each essay provides readers a description of how statistical methods are applied to solve issues in that field.
One research method that tends to generate quantitative data is official statistics. Official statistics provide both primary and secondary information, however for sociologists it provides on a secondary basis as they are already available to the public and have been retrieved by civil servants or public bodies -such as the government (Home Office), educational institutions (SQA) and health boards (NHS) and large charities. Official statistics can come in the form of either unemployment rates, death and birth dates, crime rates or marriage and divorce rates. Official statistics are often divided into two separate groups, one being hard statistics and the other, soft statistics. ‘Hard statistics’ refers to data that is compiled in a straightforward
Chapter 12 introduces the reader to the true definition of statistics, without scaring them half to death. The book breaks statistics down in two parts: descriptive and inferential. The type that is dealt with in this chapter is descriptive statistics. The simple definition of descriptive statistics are that they are just numbers in different forms, for example, percentages, numerals, fractions, and decimals. The book gives an example of a grade point average being a descriptive statistic.
5. Chose one solution and carry it out. Then ask if it has been working.
The purpose is to explain, predict and or control phenomena through a focused collection of numerical data. Answers the question what, when and where. Sampling is a large population that is random. The design is structured, inflexible, specified in detail (Quantitative, Qualitative Research, 2012). Data collection focus groups and interviews. Data interruptions are conclusions and generalizations at the end of the study, never one hundred percent sure of the outcome. Used to study individual cases and find out how people think or feel (Broader, 2010). Quantitative studies provide more in-depth information that is specific to an issue can often be used for comparison. Quantitative data offer inferential statics, a collection of data about millions of people and make inferences about a target population. The data include gender, height weight, cholesterol level, waist circumference and temperature, ages, geographical region or population and can be anonymous. It helps to measure trends over time such as frequency of outbreaks of communicable diseases in a community. Quantitative enables the ability to summarize allows for comparisons over time and across categories information sources. Quantitative has higher accuracy, eliminates bias, proves or disproves a hypothesis and narrows directions if further research is needed. Quantitative can assist nurses in determine which scientific method to determine which
Slaght, J., Harben, P. & Pallant, A., (2010). English for academic study: Reading &Writing Source Book: What is statistics?. Garnet Publishing Ltd.: UK.
Descriptive statistics is a procedure of organizing sample data. This procedure allows readers to be able to understand and describe the data’s importance. Descriptive statistics allows an individual to quickly understand the data and make predict an individual score; however, descriptive statistics does not describe all data in the sample. Inferential statistics is a process that determines whether sample data accurately represented the relationship to the population. In other words, one uses inferential statistics to determine if the sample data is believable.
For me to adequately determine which alternative is the more defensible one we must examine each question and deconstruct them, looking at what the results of accepting the question would be.
...answer the question separately but the consideration of all three together makes for stronger argument with less limitations.
What is descriptive statistics? Usually under descriptive statistics summative methods of description the data in succinct ways is considered. Data analysis usually begin with descriptive statistics, because it helps to understand what data we have – what is the sample, what is the accuracy of the data and how it is possible manage it.
...e, 2009: 34, children have a tendency to give “Yes” answers to questions more than they say “No”, therefore, he suggests that the questions be phased in a way that will encourage children to provide the most honest answer. Furthermore, in order to avoid misunderstandings, all the questions in a survey must be phrased in simple language that children can understand, in other words, this implies that researcher needs to reconstruct the survey questions in order to accommodate the level of understanding of the children which in turn can be time consuming (Riddle, 2009: 34).
A null hypothesis is when samples are taken in inferential statistics but those samples are unrepresentative because of random sampling errors. This can happen in three ways: 1. The observed difference was created by sampling errors, keeping in mind there is no bias because the survey is done randomly. 2. Null hypothesis also occurs when there is no true difference between the two groups. This meaning that the true difference is the difference a researcher would find if there were no sampling errors. 3. The true difference between the two groups is zero.