For people who are intimidated by numbers, graphs and metrics, the concept of "statistical analysis" can be daunting and even stress-inducing. However, statistical analysis is not as challenging as it seems. There are **a number of types of statistical analysis**.

These analyses are tools that can be employed to gain insight and information about everything from your sleep pattern to your red blood cell count. While data on its own is not helpful, the use of statistical analysis can change it from something that is simply a number to material that has the power to change and improve your life.

## What IsÂ Statistics?

In it's most basic definition, statistics is a mathematical discipline. Its chief concern is with the collection, analysis and interpretation of data. **Data is any kind of information or values** that are subject to qualitative or quantitative variables. For example, the following are all points of data: the number of people in a city, the number of times drivers stop at a stop sign, or the money people spend on a particular good or service.

Statistics is a set of strategies for interpreting the data, analyzing it and then arriving at conclusions that can be critical to gaining insights into behavior, habits, planning and a myriad of other work that is done in society. Scientists use data when developing medicine. **Car manufacturers use data** when deciding what features to add to a new model and which ones do to away with. Music streaming services look at data when they determine the kinds of music you play and the kind that you might like to hear.

In each of these scenarios, data is gathered and analyzed using any number of different tools or methodologies. There are **a variety of ways to examine data,** depending on the purpose of the analysis. Statistical analysis types vary depending on the goal of the researcher or analyst. Regardless of the methodology that they use; however, all statistical analysis is capable of providing valuable insight that improves quality of life.

## Is Statistical Analysis Math or Science?

Although statistics is a branch of mathematics, statistical analysis is a kind of science. By utilizing different analysis techniques and strategies, **researchers can arrive at many fascinating conclusions**. The scientific aspect is critical, however. The analysts must understand exactly what they are setting out to study, and also be careful and deliberate about exactly how they go about capturing their data.

All data gathered for statistical analysis must be gathered under the same sort of conditions if the data points are to be analyzed together. The necessity for a properly designed study, a properly chosen sample of data and the exact right type of statistical tests are the reasons why it is necessary to study statistics.

When **someone unschooled in statistical analysis** attempts a study using poorly designed data collection methods, fuzzy math or a poor analytical test, it can yield flawed or faulty data, which can lead to the erroneous implementation of changes, unethical practices, and in the case of clinical drug trials, serious health complications for study participants.

## What Are the Two Main Statistical Analysis Types?

Data itself is not particularly insightful. A list of points or information captured is **not particularly useful without high-quality statistical analysis** methods. This is how user information is extracted from the data. Speaking in the broadest sense, there are really two varieties of statistical analysis.

**Descriptive analysis is the kind of analysis** that is used to offer a summary of the collected data. It provides us with the structure of the data, the method of the data's capture and helps to describe what the data seems to say. This sort of analysis has limitations in that it can only tell us what the data is demonstrating, it cannot extrapolate anything from it. The next kind of statistical analysis is called inferential analysis.

This statistical analysis type relies on descriptive analysis to get information on exactly what the data is telling us, but it goes further. The inferential analysis examines what the data has said and **uses it to make bigger picture inferences** or a hypothesis on what that information means. This kind of inferential information may be used to improve a product, to decide where to build a hotel, to change the chemical compound of a drug or a beverage or to make sweeping policy changes in education or healthcare practices.

## What Are Some Types of Data Analysis?

There are a lot of statistical analysis types out there. The one you choose should be **informed by the types of variables** you need to contend with. When data distribution is normal, i.e., if it is in line with what is expected from the variables, you will select what is called a parametric test method. If your data is non-normal and indicates the presence of the effect of one or more variables, you will use a non-parametric testing method.

Some parametric testing methods are more useful than others. You will need to **take into account the type of study** you are doing and the sorts of results you want to measure before selecting a statistical analysis type. A correlational method examines the collected data for links between variables. A Pearson correlation scours data and tests the strength of the links between two variables that appear to be associated.

For a statistical analysis that analyzes the difference between the averages of multiple variables, you have a few options. A Paired-T test, for example, can test the **difference between the mean in two variables** that appear to be related. An Independent T-test seeks the difference between the mean in two variables that appear to be unrelated.

Sometimes data analysis needs to examine a change in data. Regression tests seek to examine if the change in one variable correlates to change in another variable. For example, **one variable in a study might be** the time at which study participants went to sleep. Another variable might be how many cups of coffee they drank. **A simple regression test would examine** whether one variable had any effect on the other, while a multiple regression test would check to see how multiple variables are brought to bear on the data.

## How Are Statistical Analysis Methods Used?

Types of statistical treatment depend heavily on the way the data is going to be used. Depending on the function of a particular study, data and statistical analysis may be used for different means. Medical scientists testing the efficacy of a drug **may employ a variety of statistical analysis methods** in order to chart various elements in the data.

Sometimes the data informs a number of things that the scientists want to discover, and so multiple methods are required to be able to gain insight and make inferences. In other cases, statistical analysis methods may simply be used to gather information about people's preferences and daily habits. User data in sites like Instagram and Facebook **help analysts to understand what users are doing** and what motivates them.

This information can be useful for advertisers who want to target a particular group of users in order to sell them things. It can also be helpful for application developers who need to know what they should change about their product, based on the users' response and habits.

## What Are Other Uses for Statistical Analysis?

Political campaigns also use data. By tracking citizens' voting history and other lifestyle choices, **politicians and lobbyists can utilize data analysis and statistical analysis** to zero in on the base of candidates to which they would like to appeal.

This can have consequences that are positive or negative. On the positive front, it can help community members coming together to canvass for a candidate who is eager to make positive change. It can also have negative consequences as with the spread of disinformation on websites that are designed to target an audience that can be influenced against a political opponent.

## What Careers Do People Use Statistical Analysis In?

People are often shocked and surprised when they discover the number of careers that employ statistical analysis methods in order to do their work. Businesses from hotels, food trucks, yarn stores, grocery stores, clothing design, music venues, coffee stands and any other commercial venture you can think of rely **heavily on inferential data** to remain successful.

Outside of the business realm, psychologists regularly conduct studies to learn about human behavior and habits. Studies that use statistical analysis methods **can help them learn about mental illness as well** as the things that people love and what keeps them healthy and happy. Governments and city planners use statistical analysis to make improvements to community safety and accessibility.

Medical science relies heavily on statistical analysis for everything from researching and **developing new medical treatments** to changing and improving health care coverage and creating new forms of vaccines and inoculations. Statistical analysis and feedback help and are necessary for almost every single profession from operating a food truck to building a rocket ship to fly to the moon.

## What Are Other Kinds of Statistical Analysis Methods?

Other statistical analysis types also exist, and **their application can play a role in everything from business to science** to relationships and mental health. Predictive analysis is an example of a kind of statistical analysis that uses algorithms to derive predictions about future behavior, based on the data that has been gathered in the past. This data is useful for marketing, finance, insurance, travel and the fashion industry.

In a prescriptive analysis, past data is analyzed using algorithms and very often **computer programs to determine the best strategy** or course of action. By reviewing the evidence that data offers, business owners and financial analysts have the opportunity to make choices for the future that seem like the best and most lucrative for their business.

Causal analysis is another critical kind of data analysis. **Causal analysis is often needed** when a business venture or other risk has failed. The failure leads the team to look at what happened so that they can try to prevent a similar failure in the future. This is the kind of data that helps individuals and businesses plan ahead so that they are more likely to set themselves up for success.

## How Is Data Analysis Different From Statistical Analysis?

Statistical analysis and data analysis are similar but not the same. Statistical analysis is a way of analyzing data. An example of this would be an exploratory analysis. This is **a kind of statistical analysis** that uses previously gathered data to try and find inferences or insights that have previously been undiscovered.

Data scientists who are analyzing statistics about city populations **may use statistical analysis** to see if there are any relationships between the areas where car thefts happen the most and **the high incidence of people** who walk to work. These sorts of connections can help to inform changes and developments in the way that you live.