Вивчаючи статистику

4. Read and translate the texts:
A statistic is a number that summarises some characteristic about a collection of data. Every data set (collection of data) tells a story, and if you use statistics appropriately, the data set will tell your story well and clearly. It is therefore important that you make good decisions about how to analyse the data you have collected for your research. Whether you have categorical or numerical data, you are going to want to know about your data and answer your questions. And, sensibly, you will begin by analysing what you have collected to:

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  make sense of the data you have gathered;
  
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  provide you with information that is easy to understand;
  
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  clarify what might be complicated;
  
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  discover relationships and differences;
  
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  make points clearly and concisely.
  

To make sense of your data, we suggest you start by presenting them as tables and graphs. Both tables and graphs will help you summarise your data, making it easier to understand. Subsequently, you can use statistics to describe individual variables, clarifying what might seem complicated, and to discover whether there are differences or relationships between variables.
Data presentation refers to the use of tables and graphs to illustrate your data. Tables are a good way to summarise categorical information. For example, suppose you have categorical data about the age and gender of people in the sales department of the firm or firms you are interested in. You might create a table with column headings such as Age, Number of males, Percentage of males, Number of females, Percentage of females, Number of employees, Percentage of employees. It will illustrate what you can do, and how it can save you a great many words of description.) What is worth drawing your attention to is that while age is strictly a numerical (ratio) variable, in this instance, because the age has been recoded into ordered categories (of age range), it has been presented as categorical (ordinal) data.
This kind of table can present a great deal of information – for instance, you can see whether or not men in a certain age category outnumber women in the same age group, or which age group has the smallest proportion of men.
Because you have collected the data on the number of men and women in your data set, you can calculate the percentages of men and women in the categories you require. But it doesn’t work the other way around – you can’t work out the number of people from percentages. For instance, if you know how many women there are in the sales department, you can work out the percentage of how many of the women are over 50. But if you were given the statistic that 10% of the women in the sales department are over 50, you have no way to work out how many women there are in the department.
You can also present your data using graphs. These also provide a simple and effective way to explore and understand your data. Graphs can be used to show the values of a single variable or the relationship between two variables. The precise graph you use depends upon the type of data variables and what you are trying to show. A multiple bar chart, has been drawn to compare the number of sales department employees (ratio) in each age group (ordinal, but recoded from the ratio data of individual employees’ ages) between genders (nominal). Within this multiple bar chart, placing the male and female bars for each age group next to each other makes comparisons between genders easy.