Extract of sample "How Statistics Is Applied in Our Everyday Life and Why It Is Useful"
In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation see our handout on how to quote. But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.
As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions.
Below you will find a useful set of hard questions to ask of the numbers you find. This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources for more information on finding reliable sources, please see our handout on evaluating print sources.
This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow.
Role of statistics for the economic and social development of a country
Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone. Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:.
All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion.
A reliable source should not exclude data that contradicts or weakens the information presented. An example can be found on the evening news.
Use of Statistics in Our Life - Words | Bartleby
If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day.
Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? This means you have no way to verify if the interpretation is in fact correct. There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question.
You may have to look to another source or sources to find all the data you need. If the author gives you her statistics, it is always wise to interpret them yourself. It is not the final word on the matter. Furthermore, sometimes authors including you, so be careful can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:. Why does this matter? However, she would no doubt point out that a this may be a spurious relationship see above and b the actual change is not significant because it falls within the margin of error for the original results.
The lesson here? Margins of error matter, so you cannot just compare simple percentages.
Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. As you write with statistics, remember your own experience as a reader of statistics. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers.
Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation. As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them. Nobody wants to be average. Because nobody knows exactly what it means.
For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average he or she is using. Otherwise, confusion will inevitably ensue. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics. No notes for slide.
Use of statistics in real life 1. Also with prediction and forecasting based on data. Statistics form a key basis tool in business and manufacturing as well. It is used to understand measurement systems variability, control processes for summarizing data, and to make data-driven decisions. Some fields of inquiry use applied statistics so extensively that they have specialized terminology. Ex- engineering statistics, social statistics, statistics in sports, etc… 4.maisonducalvet.com/llagostera-web-de-citas.php
Statistics and probability
Statistics is concerned with scientific method for collecting and presenting, organizing and summarizing and analyzing data as well as deriving valid conclusions and making reasonable decisions on the basis of this analysis. Statistical methods date back at least to the 5th century BC.
Some scholars pinpoint the origin of statistics to , with the publication of Natural and Political Observations by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general.
Today, statistics is widely employed in government, business, and natural and social sciences. Blaise Pascal, an early pioneer on the mathematics of probability. Its mathematical foundations were laid in the 17th century with the development of the probability theory by Blaise Pascal and Pierre de Fermat. Mathematical probability theory arose from the study of games of chance, although the concept of probability was already examined in medieval law and by philosophers such as Juan Caramuel.
The method of least squares was first described by Adrien-Marie Legendre in Pierre de Fermat 8. At the turn of the century Sir Francis Galton and Karl Pearson transformed statistics into a rigorous mathematical discipline used for analysis, not just in science, but in industry and politics as well. Galton's contributions to the field included introducing the concepts of standard deviation, correlation, regression and the application of these methods to the study of the variety of human characteristics Pearson developed the Correlation coefficient, defined as a product-moment, the method of moments for the fitting of distributions to samples and the Pearson's system of continuous curves, among many other things.
Karl Pearson, a founder of mathematical statistics. Today, statistical methods are applied in all fields that involve decision making, for making accurate inferences from a collated body of data and for making decisions in the face of uncertainty based on statistical methodology. The use of modern computers has expedited large-scale statistical computations, and has also made possible new methods that are impractical to perform manually. Statistics continues to be an area of active research, for example on the problem of how to analyze big data.
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