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What Is Statistics?
Learn what statistics is and why it is important to society. We’ll list the types of statistics and data as well as the importance of the sampling methods.
Sarah Thomas
Subject Matter Expert
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Statistics
What Is Statistical Analysis?
02.02.2023 • 5 min read
Sarah Thomas
Subject Matter Expert
Learn what statistical analysis is and why it’s important. We’ll list the most used types and examples and go over how to do statistical analysis.
In This Article
What Is Statistical Analysis?
Statistical analysis is the process of learning from data. Data can be any information collected through observation. Some examples include responses to a survey, measurements taken out in the field or in a laboratory, or information collected from historical records.
Two main types of statistical analysis exist: descriptive statistics and inferential statistics.
| Descriptive Statistics | Inferential Statistics |
| In descriptive statistics, you collect, summarize, and visualize data. | In inferential statistics, you collect and analyze sample data to uncover insights about a population. |
Why Is Learning Statistics Important?
You may not be aware of it, but statistics is everywhere! We use it in many fields and industries, including:
Medicine
Business
Data science
Politics
Psychology
Engineering
Physics
Whether you know it or not, you’re encountering statistics daily
Here are some everyday life examples:
Running a search on Google
Looking for a movie on Netflix
Hearing about a poll in the news
Looking up the weather
Reading predictions about which football team will win
By learning some basic statistics, you can elevate your career and personal life. Deciphering information will be easier. You’ll learn to pick apart stated facts presented in the news or in advertisements. You can even determine whether arguments made by your colleagues, family, and friends are convincing or whether they lack statistical rigor.
Purpose and Benefits of Statistical Analysis
Statistical analysis is valuable because it provides you with empirical evidence to better understand complex problems or situations involving uncertainty.
Here are 8 examples of how we use statistical analysis:
1. Business
Companies use statistical analysis to predict trends and build forecasts for the future. They also use statistics to study costs, revenue, and profits, analyze customer behavior, and test new products and features.
2. Medicine
Pharmaceutical companies and doctors run controlled experiments to evaluate the effectiveness and safety of new drugs and treatments. Hospitals and insurance companies use statistical analysis to learn about their patients and to price medical services.
3. Advertising and Marketing
An effective advertising campaign is the result of statistical studies identifying likely buyers and their interests.
4. Politics
In politics, elected officials use statistics to better understand the needs and priorities of their constituents. Politicians and their aids also use statistics to build compelling campaigns to help get themselves elected.
5. Economics
Economists use statistical analysis to study correlations and causal relationships between economic variables. For example, let’s look at an economist interested in labor laws. He might use statistical analysis to investigate the relationship between a higher minimum wage and employment rates for minimum wage workers.
6. Data Science
Data scientists combine statistics with tools from other disciplines to study digital data. Many models in data science—such as machine learning algorithms—rely heavily on statistical tools and techniques.
7. Sports
Coaches, players, and sports analysts use statistics to study the performance of athletes and teams. They use statistical analysis to predict which players and teams will win, lose, outperform, or underperform in the future.
8. Music and Entertainment
Statistical analysis is even used in the music and entertainment industries. With billions of people streaming content and listening to music online daily, the entertainment industry has endless amounts of data to work with. They use this data to discover new artists, study existing artists' popularity, and identify new and fading trends.
Common Statistical Analysis Methods
In an introductory statistics course, you’ll learn these basic descriptive and inferential statistics methods. You’ll also learn to work with at least one statistical program to help you perform statistical analysis.
Descriptive Statistics
Types of data
You’ll learn about different data types, such as categorical and numerical data.
Charts and graphs
You’ll learn to make charts and graphs to summarize and display your data.
Distributions
You’ll learn about different probability distributions, including the normal distribution.
Measures of center
You’ll learn how to calculate measures of center-like mean, median, and mode.
Measures of spread
You’ll also learn how to calculate measures of spread like variance, standard deviation, and range.
Inferential Statistics
Point estimators
Point estimators are numbers used to estimate a population parameter.
Confidence intervals
Confidence intervals are Intervals used to estimate a population parameter.
Experimental design and sampling methods
These are methods and best practices used for running an experiment and collecting statistical data from a representative sample with a large enough sample size.
Hypothesis testing
Hypothesis testing involves testing an in-going null hypothesis against an alternative hypothesis. It uses sample data, test statistics, and probability theory to determine whether the null hypothesis is likely true.
Linear regression
Linear regression is a method used to quantify the relationship between two or more variables in your data.
Causal inference
Casual inference refers to statistical techniques used to determine if a causal relationship exists between two or more variables in your data.
One-way ANOVA
One-way analysis of variance (ANOVA) is a statistical test used to determine whether two or more sample means are statistically different.
Categorical data analysis
These are analysis tools explicitly designed for analyzing categorical data.
Randomized control trials
Experiments designed to isolate and find causal relationships
Statistical Software
Statistical analysis software are computer programs that collect, process and analyze data to identify patterns and trends.
Researchers and data scientists use these tools to make informed decisions and uncover insights from complex data sets.
Some examples are:
Excel or Google Sheets
R
STATA
SPSS
SAS
How To Do Statistical Analysis?
Any statistical process has these four main steps.
1. Identify Your Question
Statistical analysis begins with a question. What is it you want to learn? Once you have a question you want to answer, it also helps to formulate a hypothesis about what the answer to your question might be.
2. Collect Your Data
After you’ve identified your question, the next step is data collection or searching for existing data to help you answer the question. For example, let’s say you're a business manager. You want to know more about your customers. A good option is to collect data from a survey, run an experiment, or look over your past sales data.
It’s worth noting sometimes Step 1 and Step 2 of the statistical process happen in reverse. Finding an exciting data set can sometimes motivate you to come up with a great question.
3. Analyze Your Data
With your data and question in hand, the next step is identifying and using relevant statistical methods. You might use methods in descriptive statistics to summarize and visualize your data. If you want to take your analysis a step further, you can use methods like hypothesis testing and regression to find evidence for or against your in-going hypothesis.
4. Draw a Conclusion
The final step in the statistical process is to reflect on what you’ve learned. Did your statistical analysis provide evidence to support your in-going hypothesis, or did it provide evidence against it? How convincing were your results? Were they statistically significant?
Outlier instructor of Intro to Statistics, Ashley Carroll, gives an overview of the statistical process:
Now you have a better grasp of statistical analysis. You can gain even more practical skills on your own time with Outlier’s Intro to Statistics course. Transfer the credits toward a degree or use your new abilities in your everyday life. Or even better. Both.
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