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The four step process of statistics
A collection of statistics shows that the average American spends more time listening to music than reading. This data was obtained from an iphone survey and shows that the average person listens to 1 hour and 18 minutes of music per day, while they spend just under 20 minutes reading. The population in this age group spends nearly 45 hours a week on music, while adults spend just over 16 hours on books. There is a significant link between the amount of time spent listening to music and reading. This data was collected from a survey of music and book reading, but could also be collected from other sources. The data is, however, reliable and truthful.
The four step process of statistics is a method used to define the four logical steps needed to produce a valid research question. This method consists of four distinct steps that are needed to create an accurate base for the information being researched. The first step in this process defines the population that is being researched, while the second step defines how the sample will be obtained. The third step is to gather all of the information from the research group. The fourth step is to analyze the collected data, and come to a conclusion about it. In this process there are four distinct steps that have been completed.
There are four logical steps in statistics:
1. Define population
2. Define sample
3. Gather and analyze data
Describing data
Describing data is also a difficult task when you are trying to compare one set of data with another set. Most people make this mistake when they attempt to describe where the time that they are spending on magazines compares against the time that they are spending watching TV. In this case, people use words such as “more” and “less” to describe how much time they spend on TV or magazines. These words are not very useful, though. To start with, they do not tell you how much of the time is spent reading or watching TV. Also, these words can be misleading and have no meaning in the context that they are used in. Below are some more effective descriptive techniques.
Analyzing data
Analyzing data is the third stage of the process. This is where you actually begin to make decisions about your data. One way that people use to analyze their data is to compare it against a set of values. A good example of this analysis is comparing the spread of hours per week that people spend on reading and looking at magazines against the number of hours they spend listening to music. This is the practice of quantifying a variable. The following shows an example of the quantification process:
(A) Hours per week spent reading
(B) Hours per week spent listening to music (1) (2) Hours per week spent reading and looking at magazines (C) Total number of hours
Drawing conclusions from data
In the final stage of the process, you are actually drawing conclusions from your data. This is where you most often misinterpret your data and make incorrect decisions. The conclusions that you draw from your data will vary based on the type of analysis that you have carried out. You may decide that people who listen to music for 30 minutes a day are more likely to read books than people who listen to thirty minutes. The problem with this type of conclusion is that you do not know the ages of the people in this analysis. If you are comparing a group of old people to a young group of people, you may find that there is no significant difference between the two groups since most old people never read books. You need to be careful that you do not jump to conclusions. If your data does not show a significant difference between two groups, one group being older than the other does not mean that there is no relationship between your two variables. There may in fact be a significant relationship between the two variables but only within a certain age group. Your conclusion can only be true if the data shows that there is no significant relationship within the age groups and no significant difference between the age groups.
You will have to decide whether you want to draw a conclusion based on the results of your analysis. In statistics there are three ways that you can go about drawing conclusions from data. These are called hypothesis testing, statistical inference and logical inference. The results of each type of inference should not be taken as gospel, but rather they should be used to determine what the significance level of your findings are.
