You’re probably already familiar with the term statistics or your interest only begins to grow and you want to learn more about it? So, what is statistics? Statistics is a branch of mathematics. Statistics is all about collecting, organizing, analyzing big chunks of raw data and, finally, presenting them in a meaningful way. This is extremely important discipline as it helps people to understand the world around us better with the help of numbers. When we want to research this or that problem using surveys or experiments without applying statistics principles, we would never be able to interpret results adequately
Observational vs. Experimental Types of Studies
In order to test different hypothesis, researchers apply different types of statistical study. There are two the most used types of studies applied in statistics. They include Observational and Experimental studies. Let’s now have a closer look at each of them.
Observational Study
During the observational study, the researcher simply observes the objects of the study, in other words, statistical population. The researcher cannot interfere with the process, make any tweaks to it or influence it in any way. All he has to do is observe. Let me show you a very simple example of the observational study for you to understand it better.
Imagine you’re walking down the street and someone approaches you. The man asks you if you mind answering a few questions like “who you’re gonna vote for next presidential elections” or “do you have a Master’s degree?” The man simply collects the data, he cannot change your answers in any way, he just writes them down to analyze, interpret, and present them to masses.
Experimental Study
Unlike the observational study, the experimental study requires intervention from the researcher’s side in order to test the hypothesis. In most cases, experimental studies are randomized. It means that subjects are grouped randomly.
Experimental Study employs RCT model (Randomized controlled studies). Suppose you want to test what effect smoking has on women. You take 100 women aged 30 and divide the group in half. The first group will smoke a pack of cigarettes during a month while the other group won’t smoke. After that, the researcher will be able to analyze and interpret the data.
Statistical Methods and Data Analysis
The raw data will have no value without proper statistical methods for data analysis. There are many various methods to make the use of data but we’re not going to cover all of them but two most popular ones.
Descriptive Statistics
Descriptive statistics involves the analysis of data and helps to describe and visualize data in such a way that we can see general patterns emerging from the data. Though we cannot make or reach conclusions based on descriptive statistics as it’s just a simple way to present the data. Nevertheless, it’s an extremely important method of data analysis because when we have loads of raw data and we want to make use of it and see some meaningful trends, then descriptive statistics is a must.
Inferential statistics
Inferential statistics deals with more isolated chunks of data. Using inferential method, we take a random sample to describe the trends of the bigger population. For example, you want to find out what grades on average the US students have in math. It would be impossible to gather the data from each student across the US. That’s why you take a random sample, say, 1000 students, from different schools and cities collect, interpret, generalize and apply the data to all student population. That’s the inferential statistics put in simple terms.
I don’t understand statistics. What should I do?
No one tells that statistics is an easy topic but practice makes perfect. If you feel like the information you’ve just read is overwhelming, don’t worry that’s okay. Sometimes students have to study statistics at the university, however, they don’t like or understand it. In this case, you can seek help from one of our professional statistics homework helpers.
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