First, all data have both an objective and a subjective component. Numbers can be easily assigned to all qualitative data (such as open-ended questions in surveys), and any number obtained by a quantitative study is interpreted using a subjective or qualitative judgment. Second, using differen...
Sekaran and Bougie (2011) stated that each member of population has a known zero probability to be selected as sample subjects. For this study, researcher used a probability sampling method where all the respondents in population have a probability to be chosen as a subject.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
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.
Based on a randomly selected group of 500 patients with high cholesterol, it was found that 67% have heart disease. Is this a population or a sample; explain your answer.
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
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.
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).
Random representative sampling is a method of sampling that uses random selection to obtain its samples. By making sure that everybody has an equal chance at being selected, random representative sampling ensures diverse samples. Using the example in paragraph one, a random representative sample allows you find the statistics on all the company’s employees without interviewing all them. Random representative sampling is important for getting accurate poll results because it allows you to find the view of a population while making sure that the poll is not biased in any way.
Sampling is the method scientists use to collect people, locations and other items to research. The outcome of sampling research is only as valid as the samples the researchers used for their studies. A sample is part of a whole called a population. It is possible to sample entire populations, such as in the United States census, but for most applications this is impractical.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
Two of the most useful types of statistics are known as descriptive and inferential statistics. Descriptive statistics is the term that is used to describe the analysis done to summarize the data from a population in a meaningful way; typically, through graphs and charts. On the other hand, inferential statistics is a way of making generalizations about a population of interest from a small sample size (Descriptive and Inferential Statistics, n.d.).
Descriptive stats summarize data so the data can be comprehended. The researchers prepare a frequency distribution which shows the frequencies as descriptive statistics. Percentages, and averages are also descriptive statistics. Therefore, the descriptive statistics describe sets of data collected through observation. Then the statistics are organized in tables, pie charts, graphs etc. Researchers must be sure the kind of descriptive statistics matches the kind of data that has been collected.
As Chiromo 2006: 17 correctly points out, there are two types of sampling techniques namely probability and non-probability sampling. Probability sampling is the type of sampling that affords each member or unit of the population an equal choice of being included in the sample, (Clark 2006: 18). On the other hand in non-probability sampling, the units of the sample have an unknown chance of being included in the sample. Quantitative research uses both random and non-random sampling although there is usually a mistaken belief the non-random sampling is for qualitative research alone.