Learn inferential statistics with online courses and programs
What is inferential statistics?
Inferential statistics is a branch of applied mathematics focused on drawing reliable conclusions about the characteristics of a population, based on a sample. A statistician studies the qualities of a sample, such as sample size and distribution. Then, they can determine the likelihood that sample statistics, such as mean and variability, are accurate of the broader population from which the sample is drawn.
Through this process, statisticians can make generalizations about large groups using a reasonable amount of data.Footnote 1
Some examples of inferential statistics include consumer research, political polling, and drug trials.
There are two primary types of inferential statistics:
Hypothesis testing: Determining if there's enough statistical evidence to accept a certain belief, or hypothesis, about a data set.Footnote 2
Regression analysis: Determining whether particular variables influence characteristics of a data set or outcome.Footnote 3
Inferential statistics is one of two main types of statistical analysis — the other is descriptive statistics. Descriptive statistics is concerned with describing the basic features of collected data. To explain the difference between descriptive and inferential statistics, it may be helpful to use a simple survey example.Footnote 4
A statistician might survey 100 shoppers at the mall and ask why they are there. Descriptive statistics would explain that 55 people surveyed went to the mall to make returns, 21 to shop, and 23 for some other reason. Inferential statistics will study the characteristics of those 100 people and the survey and might conclude that U.S. shoppers are significantly more likely to go to the mall to make returns than to shop.
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Why learn inferential statistics?
Whether it's descriptive or inferential statistics, knowledge of this field can be beneficial for professionals and organizations. A robust understanding of measurement, survey methodology, hypothesis testing, and confidence intervals can help individuals to:Footnote 5
Effectively perform research
Read and interpret technical literature
Improve critical thinking and analysis
Know when to seek additional statistical assistance
Additionally, inferential statistics training applies to all kinds of industries and fields. Through this kind of measurement and testing, businesses and organizations can draw conclusions about everything from customer preferences and user trends to voter turnout.
Inferential statistics course curriculum
The curriculum for an online statistics course may depend on the goal of the course. For example, a statistics for data science class could focus on different concepts and applications than an introductory inferential statistics tutorial. Topics covered in a fundamentals of inferential statistics course may teach learners how to:
Setup and perform hypothesis tests
Perform linear, multiple linear, nominal, and ordinal regression tests
Calculate confidence intervals
Perform parametric and non-parametric tests
The individual concepts of inferential statistics can apply to many industries. For example, email marketers perform A/B testing, a form of hypothesis testing also known as split testing. A/B testing entails sending two or more iterations of an email to random samples of registered recipients to determine what changes to the emails increase engagement. While this process can glean results without statistical analysis, an online course could provide context and skills to perform stronger, more robust kinds of tests.Footnote 6
Explore jobs that use inferential statistics
Examples of inferential statistics in the workplace can be found in fields as diverse as government, tech, healthcare, research and development, marketing, and more. Whether they are using descriptive or inferential statistics, statisticians perform many duties as part of their roles across industries. Some of those tasks include:Footnote 7
Determining what data may help solve those problems
Designing methods of collecting data
Conducting experiments to acquire data
Interpreting the data
Communicating results to different audiences
Many statisticians have a bachelor’s or master’s degree in mathematics, which may also be complemented by other fields of study depending on the type of statistics performed. For example, someone interested in data science and statistics may complete a data science boot camp.
Alternatively, someone interested in process improvement, particularly the Six Sigma methodology, may have a statistics certification such as the Six Sigma Yellow Belt Certification or the Six Sigma Green Belt Certification, which can be earned by taking an online statistics course.Footnote 8
How to become a statistician online
There are ample opportunities to take an inferential statistics online course and earn a statistics certification. Classes may provide an in-depth study of descriptive or inferential statistics, different types of inferential statistics methods and theories, or more specialized training like statistics for data science or Six Sigma process improvement.
For many who already have experience in statistics, it may be appealing to get a masters in data science or take a data science boot camp to further unlock the combined power of data science and statistics. Alternatively, someone may have a bachelor’s in data science and take a deeper dive into statistics for data science.