Data analysts are exposed to lots of data from time to time. The challenge is sifting through voluminous data to interpret the ramifications. There are several tools and methods that are used, especially in statistical data analysis.
In a world where big data is coming full circle, there are several tools that can help you reduce your workload, while at the same time improving your efficiency and reliability of the data you use. The methods discussed herein are the foundation of data analysis. Once you master them, it is easier to graduate into sophisticated methods and techniques:
● Standard deviation
Standard deviation is an expression of how far data spreads from the arithmetic mean. Standard deviation in data analysis is about data point dispersion from the mean. A high value shows a large spread from the mean, while a low value means that most of the data in use is close to the mean.
Always use standard deviation alongside other techniques to derive conclusive results from your study. Without this, especially with data sets that contain many outliers, standard deviation is not a good value determinant.
● Averages
This refers to the arithmetic mean. You arrive at this by dividing the sum of (n) items on your list by the number of (n) items on the list. Averages help you understand the general trend in a specific data set. Calculating averages is very easy, and from this information, you can tell so much about a given data set at a glance.
Even as you use averages, you must be careful not to use them in isolation. Independent of other methods, averages can be misconstrued for the same information available from median and mode. If you are working with data that has a skewed distribution, averages are not the best option because you don’t get information accurate enough to support our decision-making needs.
● Regression analysis
Regression analysis is about identifying the relationship between different variables. From these relationships, you will then establish the dependency between the variables. This analysis helps you identify whether relationships between variables are weak or strong.
Regression analysis is usually a good option when you need to forecast decision making. Since they consider the relationship between dependent and independent variables, you can look at many variables that affect your business in one way or the other. The dependent variable in your study refers to the variable you need to understand. The independent variables are endless, and could represent any factors you are looking at, which might affect the dependent variable in some way.
● Hypothesis testing
This method is also referred to as t testing. In hypothesis testing, the goal is to test a given assertion to determine whether it is true or not for your study population. This method is popular in so many areas that are reliant on data, like economics and scientific and business research purposes. There are several errors that you must be aware of if your hypothesis study is to be a success. One common error in hypothesis testing is the Hawthorne effect, also known as the observer effect. In this case, the results of the study do not reflect the true picture because the participants are aware they are under observation. As a result, the results are often skewed and unreliable. Hypothesis testing helps you make decisions after comparing data against hypothetical scenarios concerning your operations. From these decisions, you can tell how some changes will affect your operation. It is about the correlation between variables.
● Determining sample sizes
You need to learn how to select the right sample size for your studies. It is not feasible to collect information from everyone in the study area. Careful selection of your sample size should help you conduct the study effectively. One of the challenges you might experience when choosing the sample size is accuracy. While you are not going to study the entire population of interest, your sample must be randomly selected in a manner that will allow you to get accurate results, without bias.
Here I am ending this post. Make sure you have good understanding of the discussed Methods Used in Data Analysis you delve into data analytics. In the next post we'll discuss about Types of Data Analysis.
In a world where big data is coming full circle, there are several tools that can help you reduce your workload, while at the same time improving your efficiency and reliability of the data you use. The methods discussed herein are the foundation of data analysis. Once you master them, it is easier to graduate into sophisticated methods and techniques:
● Standard deviation
Standard deviation is an expression of how far data spreads from the arithmetic mean. Standard deviation in data analysis is about data point dispersion from the mean. A high value shows a large spread from the mean, while a low value means that most of the data in use is close to the mean.
Always use standard deviation alongside other techniques to derive conclusive results from your study. Without this, especially with data sets that contain many outliers, standard deviation is not a good value determinant.
● Averages
This refers to the arithmetic mean. You arrive at this by dividing the sum of (n) items on your list by the number of (n) items on the list. Averages help you understand the general trend in a specific data set. Calculating averages is very easy, and from this information, you can tell so much about a given data set at a glance.
Even as you use averages, you must be careful not to use them in isolation. Independent of other methods, averages can be misconstrued for the same information available from median and mode. If you are working with data that has a skewed distribution, averages are not the best option because you don’t get information accurate enough to support our decision-making needs.
● Regression analysis
Regression analysis is about identifying the relationship between different variables. From these relationships, you will then establish the dependency between the variables. This analysis helps you identify whether relationships between variables are weak or strong.
Regression analysis is usually a good option when you need to forecast decision making. Since they consider the relationship between dependent and independent variables, you can look at many variables that affect your business in one way or the other. The dependent variable in your study refers to the variable you need to understand. The independent variables are endless, and could represent any factors you are looking at, which might affect the dependent variable in some way.
● Hypothesis testing
This method is also referred to as t testing. In hypothesis testing, the goal is to test a given assertion to determine whether it is true or not for your study population. This method is popular in so many areas that are reliant on data, like economics and scientific and business research purposes. There are several errors that you must be aware of if your hypothesis study is to be a success. One common error in hypothesis testing is the Hawthorne effect, also known as the observer effect. In this case, the results of the study do not reflect the true picture because the participants are aware they are under observation. As a result, the results are often skewed and unreliable. Hypothesis testing helps you make decisions after comparing data against hypothetical scenarios concerning your operations. From these decisions, you can tell how some changes will affect your operation. It is about the correlation between variables.
● Determining sample sizes
You need to learn how to select the right sample size for your studies. It is not feasible to collect information from everyone in the study area. Careful selection of your sample size should help you conduct the study effectively. One of the challenges you might experience when choosing the sample size is accuracy. While you are not going to study the entire population of interest, your sample must be randomly selected in a manner that will allow you to get accurate results, without bias.
Here I am ending this post. Make sure you have good understanding of the discussed Methods Used in Data Analysis you delve into data analytics. In the next post we'll discuss about Types of Data Analysis.
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