Statistical analysis allows you to use math to reach conclusions about various situations. This type of analysis can be performed in several ways, but you will typically find yourself using both descriptive and inferential statistics in order to make a full analysis of a set of data. There are key differences between these two types of analysis, and using them both can aid you in getting accurate conclusions about your test subjects.
So where is this type of analysis applicable? Believe it or not, most people use statistical analysis for many aspects of their life whether they realize it or not. You can really enhance your understanding of the world with just a little more understanding of statistics and how it works. If you want to learn all there is to know about the subject beyond the basics of descriptive and inferential statistics, then check out the Workshop in Probability Udemy course.
In order to understand the key differences between descriptive and inferential statistics, as well as know when to use them, you must first understand what each type of statistics does, and what it is used to analyze.
Descriptive statistics is a form of analysis that helps you by describing, summarizing, or showing data in a meaningful way. An example of descriptive statistics would be finding a pattern that comes from the data you’ve taken.
The limitation that comes with statistics is that it can’t allow you to make any sort of conclusions beyond the set of data that is being analyzed. Descriptive statistics only give us the ability to describe what is shown before us. You cannot draw any specific conclusions based on any hypothesis you have with just descriptive statistics.
A good example would be grades for a body of students. Imagine you were grading 100 student’s exams and you were analyzing the averages of the student’s test scores. Using just descriptive statistics, you can find patterns of the test scores, such as a small number of students get high and low test scores and a large number of students get average test scores.
Unfortunately, descriptive analysis doesn’t give you the ability to go beyond this set of data. For example, you wouldn’t be able to figure out what the averages of the next 100 test scores would be.
There’s a great deal of importance that comes with descriptive statistics. If you were to simply present the data as it is, then you would not be able to easily visualize what the data is trying to show or tell you. This is even more difficult when you have a lot of data to process.
Descriptive statistics has a lot more depth to it than you may think, and there’re a lot of great resources for you to use to learn about it. Check out the Udemy course Descriptive Statistics in SPSS, which teaches you how to do this form of analysis with computer software.
The Two Types of Descriptive Statistics
There are two types of descriptive statistics that people tend to use when they’re analyzing their data. The first type is the Measures of Central Tendency. This type of statistics describes the central position for a frequency distribution when it comes to a specific group of data.
The way that you can describe this form of statistics is through finding the mean, median, and mode of the data that you’ve collected. In reference to the 100 exam grades, finding out that the average test score is 77 would be a measure of the central tendencies of the data.
The second type of descriptive statistics that’s typically used is Measures of Spread. This type of analysis helps people summarize data by describing the way in which the data is spread out. Look at the test scores again; the median score may be 83.
This form of analysis takes a look at these test scores and evaluates how many students made a score between 83 and 100, as well as how many made scores between 0 and 83. The different ways in which to do this form of analysis includes finding things such as the absolute deviation, variance, quartiles, standard deviation, and the range.
Some of these concepts may seem complicated, but you can learn statistics quickly and easily with Udemy. There are all sorts of courses and tutorials out there to help you master statistics.
Above we explore descriptive analysis and it helps with a great amount of summarizing data. The examples regarding the 100 test scores was an analysis of a population. A population is a group of data that has all of the information that you’re interested in using. A population can be either large or small, depending on what you’re analyzing.
When you use descriptive statistics, you have to have the entire population at your disposal, since descriptive analysis gives you the properties of the population as a whole, such the mean or the absolute deviation. These are called parameters, and with only a small bit of the population you can’t suddenly come up with the parameters.
Inferential statistics comes into play when you don’t have access to the entire population. For instance, if you wanted to find the average of the entire school’s test scores you might find it impossible for you to do so in order to get the data that you want.
Instead of getting the data from the entire school, you would take a small sample, such as the 100 test scores that you already have. This small sample will allow you to make inferences about the entire population of the school, and even though you don’t genuinely have all of the data, you can make an educated guess on what that data may be.
When you’re finding things, such as the standard deviation or range, you aren’t finding parameters like you did with descriptive statistics, but instead you’re finding statistics.
The technique you use for inferential statistics is a bit different from the ones you use with descriptive statistics. Inferential statistics involves you taking several samples and trying to find one that accurately represents the population as a whole. You then test that sample and use it to make generalizations about the entire population, which in this case is every student within the school.
There are two methods used in inferential statistics: the first involves estimating the parameter and the second involves testing the statistical hypothesis.
The one downfall to inferential statistics is that your data won’t be accurate. You can come to a close estimate of what the test scores of the population will be like, but you have no way of accurately knowing what the parameters of the test scores truly are without having the data yourself.
As with descriptive statistics, you can learn to do inferential statistics with computer software. This can make things it a lot easier and will allow you to input data for a much larger set of numbers. Check out the Udemy course Inferential Statistics in SPSS. This course teaches you everything you need to know about doing inferential statistics with the SPSS software. You will also get a step-by-step guide to help ensure that you are able to learn the concepts with ease.
The Differences of Descriptive and Inferential Statistics
Both descriptive and inferential statistics have their benefits and shortcomings. Descriptive statistics are great for a small population. You can accurately produce numbers for the population without worrying about being off or making any errors, but you can’t make any conclusions that go beyond the population that you have. As mentioned before, you have the accuracy that you may want, but it is all limited to a very small population, at least in comparison to inferential statistics.
With inferential statistics, you don’t need the data of the entire population to make your conclusions; this level of statistics only needs accurate samples in order for you to draw your conclusions. You can make an educated guess on what the parameters of the entire population are, no matter how large it may be.
Unfortunately, this does prevent you from having accurate data. Although inferential statistics does give you a good guess of what the data may look like, it doesn’t compare to the accuracy that you will get with something more concrete, as with descriptive statistics.
Both forms of statistics are great and when they’re used together you can get accurate parameters of a small population, and then take those parameters further and get great approximations of what a much larger population’s statistics are.
Understanding Key Types of Statistics
As easy as learning the concepts of statistics may seem, it can be a difficult thing for someone to apply in a real world situation. Having a few dozen pieces of data to analyze may not be much of a task, but when that data reaches into the hundreds or thousands, things can become a bit more difficult.
If you’ve never done any sort of statistical analysis before, try the Introductory Statistics course available on Udemy. It can teach you many of the key concepts of statistical analysis.