Statistics is an interesting course that involves a series of research, survey, data collection, quantitative and qualitative data sorting, as well as data analysis which is then applied to daily life.

When it comes to statistics, there are different methods of data analysis, in which collected data samples are analysed using analytics tools such as Stata and SPSS.

We will be showing in this article the different methods of data analysis that will help you choose the most suitable methods for your data sets as a data analyst.

After making your survey and research, which leads to collecting your data, the next thing you'll want to do is sort out your data and analyse them. The implication of sorting and analysing data will help you understand what each of your variables stands for and what exact data analysis method will suit them, depending on your research topics.

There are two major data types in statistics that you can sort your data into. These are qualitative data and quantitative data. After sorting your data into these two types, you can test and interpret them into meaningful results.

Once you've sorted out your data, you can go ahead to explore these data to get some descriptive statistics from them. You can do this through numerical representation ( mean, median, mode) or through graphical designs and representations of data ( pie charts, bar charts).

After the exploratory analysis of your data, you can check if it aligns with the hypothesis that each test requires. This will help you know the suitable test to perform on your data so that you can use the suitable analysis method for your data set.

Read up on everything you need to know about learning data science.

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Hypothesis Testing

A hypothesis is a summary related to the information set that data analytics methods can test. It is also a statement about a set of data. On the other hand, hypothesis testing is a research method used in testing the probability of how true a statement is. Dealing with research project topics requires you to search online and do some study. So, hypothesis testing is also not left out.

Date representation
A visual representation of your data will help you understand them better || Image Source: Unsplash

Hypothesis testing is a systematic way of knowing and determining if the result gotten from a research study can be applied to real-life situations. All you need is your data set to evaluate your hypothesis about it.

Hypothesis testing helps to provide a connection between the underlying theory and specific research questions. It helps analyse data and measure the validity and reliability of your research. Also, it serves as evidence to prove the validity of the research.

There are two major types of hypothesis testing:

Null Hypothesis

This statistical theory suggests that there is no statistical relationship between a given set of a single observed variable. It shows that there is no difference between certain features of a population.

Alternative Hypothesis

This is just the opposite of null theory, and it shows your observations and result of real effects.

Also, the following are the criteria of a research hypothesis:

  • It must be precise and clear
  • It must go through the verification process

Here's how to get started with statistical methods.

Different Methods of Data Analysis

Different type of data set requires different methods for analysing them. Some data set may require one method of analysis, while others may require two or more. All you need to do is to understand the type of data you're working with and the suitable data analysis methods for them.

Exploratory Analysis

This is usually the first step in analysing your data. You explore large data set in no specific order to discover hidden patterns, which is then applied to solve real-life problems.

For instance, there are different restaurants in Lagos, Nigeria, and different people patronise these restaurants. With adequate data, you can always find out the restaurant with more customers or a restaurant that a typical Nigerian will patronise.

This method can be used by companies business management to know their status in the industry and to stay above their competitors. It can also help them view the state of their business and how to improve their business strategies.

Furthermore, the exploratory data analysis method can also be used with other data analytics methods and tools such as graphical designs and representations.

The following are steps to follow in carrying out an exploratory data analysis:

  • Get full information about your data set
  • Discover hidden data patterns
  • Get important variables from your data set
  • Check for errors and mistakes
  • Test your hypothesis
  • Record your results
  • Qualitative Analysis

Observation is one of the important skills you must have for qualitative data analysis. It involves identifying, examining, and interpreting data patterns and how these patterns can help in real-life situations.

Black woman studying a chart
Discover hidden patterns with data analysis methods || Image Source: Pexels

To work with this method as a data analyst, you have to observe your data closely. This method involves sorting out data categorically, which means sorting out data based on a particular attribute.

The following are the processes for performing a qualitative analysis:

  • Collect your data set
  • Organise your data
  • Set up codes for the collected data
  • Verify your data
  • Present your data systematically.

Quantitative Analysis

This is a very prominent method in data science. If you're also learning statistics as a course, you must be good with this aspect.

Quantitative data analysis involves analysing data based on the number in which your data is converted while retaining its properties and meaning. It involves calculation, measurements, statistical and mathematical data analysis since it deals with numbers.

The following are steps to achieve a quantitative analysis:

  • Start with a science theory
  • Make your hypothesis
  • Craft your research design
  • Collect your data
  • Analyse your data
  • Verify your results
  • Publish your result systematically

Descriptive Analysis

This analytics method characterises the world by answering the questions: Who? What? When? Which? Where?

Descriptive analysis shows and summarises data sets in a useful way based on patterns that may emerge from studying a data set used in your project. It is one of the methods used for conducting statistical data analysis.

Learning and knowing how descriptive data analysis works give you the advantage of distributing, detecting an error, and identifying patterns among variables, making it easy for you to conduct statistical analysis on your data.

This method is very useful in our world, especially when viewing survey items' responses.

You can follow these steps to get your description data analysis done:

  • Know the aim of your analysis
  • Collect your data sample
  • Sort out your data
  • Analyse your data
  • Interpret the results
  • Publish your results.

Predictive Analysis

Predictive analytics is typically about getting information from data to predict trends and patterns. It is an advanced analytics that is used in the development and predictions of future occurrences and outcomes.

You can do this by using historical data in conjunction with statistical models, data mining techniques, programming, and machine learning.

Most of the companies in Lagos, Abuja and other parts of Nigeria use predictive analytics to discover hidden patterns in a data set to identify risks and opportunities in their business and industry.

This helps companies know how to work better on their goods and service and how well a new good or service will perform.

Research method
Always endeavour to describe your research methodology in your report || Image Source: Unsplash

As a data analyst, you'll be able to predict all possible outcomes from a particular data set with this analytics as long as you consider all relevant factors and follow the process below:

  • Collect your data
  • Explore your data
  • Analyse your data
  • Develop the needed model
  • Apply your model
  • Verify your results

Read on how to solve statistics problems!

Why are Data Analysis Methods Important?

Using different analysis methods is very useful, especially in today's world. It's important in business management, finance, research, science research, and other professional fields.

As a Nigerian business owner, using the suitable data analysis method helps you understand your business, your customers, and your competitors in the industry.

With data analysis, you can know the behaviour of each of your clients, what platform they often use, what they are interested in, their mode of buying things, how social they are, and more. This will help you create your products ( goods and services) in a way that suits your customer's needs which will improve your sales and overall business.

If you're working in the management sector of a company in Nigeria, then you should have skills in different data analysis methods.

Knowing different data analysis methods will help you know when and where to invest the company's money. It also helps you predict future income and gains and future problems and obstacles, thus improving the growth and development of the company at large.

People of different professions can use data analysis. You will always be able to get useful information about what you're working on, as long as you use the suitable data analysis methods for your data set. If you need more explanation about the data analysis method, you can search online for videos ebooks. You can also buy a course or get an experienced statistics tutor in that view.

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Emmanuel

Emmanuel Oyibo is a Content Writer and budding Python Developer. He's adept at many niches, but his expertise shines more in the medical sciences, tech, and blockchain niches. Emmanuel has a Bachelor's degree in Biochemistry, and he's currently a full-time freelance content writer. When he's not working, he's probably absorbed in some television adventure series or a good book.