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"differences between descriptive statistics and inferencial statistics
Merits of descriptive statistics
Advantages and disadvantages of descriptive and inferential statistics
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Statistics Statistics can be defined as the scientific study of numerical data based on variation in nature. It deals with the collection, compilation, presentation, analysis and interpretation of numerical data. Biometry Biometry is concerned with the application of statistical methods to the solution of biological problems. Population In statistics we concentrate on the collection of values of the characteristic, or record of a qualitative trait, evaluated for each unit in the population. We call this the statistical population or simply the population. A population refers to complete set of individuals, objects or measurements having some common observable characteristics. In other words, the population can be defined as a collection, …show more content…
These are commonly known as descriptive statistics. Nowadays, a major emphasis of the statistics is the evaluation of information present in data and the assessment of the new learning gained from this information. This is the area of inferential statistics and its associated methods are known as the methods of statistical inference. Descriptive statistics help summarize the sample. Procedures for statistical inference allow us to make generalizations about the population from the information in the sample. Thus, the field of statistics can be divided into two general areas, descriptive statistics and inferential …show more content…
Thus, descriptive statistics consists of methods and procedures for presenting and summarizing data. The procedures most commonly employed in descriptive statistics are the use of tables and graphs, and the computation of measures of central tendency and variability. The purpose of descriptive statistics is to summarize or display data so we can quickly obtain an overview. Inferential statistics employs data in order to draw inferences (i.e., derive conclusions) or make predictions. In inferential statistics sample data are employed to draw inferences about one or more populations from which the samples have been derived. Inferential statistics allows us to make claims or conclusions about a population based on a sample of data from that population. Data and
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.
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
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
1) Demographic is the statistics of the human population using variables such as age, income, education, religion ethnicity ECT.
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
A researcher determines that 42.7% of all downtown office buildings have ventilation problems. Is this a statistic or a parameter; explain your answer.
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
On the other hand, Quantitative research refers to “variance theory” where quantity describes the research in terms of statistical relationships between different variables (Maxwell, 2013). Quantitative research answers the questions “how much” or “how many?” Quantitative research is an objective, deductive process and is used to quantify attitudes, opinions, behaviors, and other defined variables with generalized results from a larger sample population. Much more structured than qualitative research, quantitative data collection methods include various forms of surveys, personal interviews and telephone interviews, polls, and systematic observations. Methods can be considered “cookie cutter” with a predetermined starting point and a fixed sequence of
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
Quantitative studies are primarily numbers based. They deal with large cohort groups as well as analyze large amounts of data. “A quantitative researcher typically tries to measure variables in some way, perhaps by using commonly accepted measures of the physical world (e.g., rulers, thermometers, oscilloscopes) or carefully designed measures of psychological characteristics or behaviors (e.g., tests, questionnaires, rating scales)” (Leedy & Ormrod, 2010, p. 94).
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.
Quantitative research involves the collection and converting of data into numerical form to enable statistical calculations be made and conclusions drawn. It provides a measure of how people think, feel or behave and uses the statistical analysis to determine the results. However, this measurement results in numbers, or data, being collected, which is then analyzed by using quantitative research methods (Byrne, 2007).
c. Statistical Design: It concerns with the question of how many items are to be observed and how the information and data gathered are being