How Do We Ensure Accurate Data

Statistic Brain is an educational institute that researches and analyzes mainstream areas of interest to compile statistics and re-publish factual information to cultivate an environment of learning that can benefit future generations. Statistic Brain predominant goals are to:

» Ensure that our content is unbiased, accurate, and credible. Our statistics are updated in real-time as there are changes to current events. Statistic Brain’s corporate policies and procedures dictate that each employee shall aggregate research from multiple sources including: varying news outlets, think tanks and white papers, focus groups, social media, industry standards and benchmarking, subject matter experts (SME’s), internal research, etc.)

» Create an innovative platform design that presents statistical data to users in an effortless format. Statistic Brain’s website is unique from its competitors because it focuses on the ease of user access to locate, understand, and utilize free statistical content. Statistic Brain’s mission statement includes an unprecedented dedication to promoting educational statistical research. This research benefits any individual seeking statistical knowledge, students, journalists, professors, small business owners, investors, government agencies, and publications such as the Oxford Press, Forbes, Wall street Journal and many others.


A reputable source must have a long standing reputation for publishing data that has been proven and verified by another outside source. When data is incorrect, the source must attempt to correct and disperse the new data.

The source must have a physical address along with the ability to reach someone who can verify the published data.

The sources data must have a broad enough test group and large enough test size to be deemed accurate.


We collect data using 4 main methods; Online surveys, Phone surveys, In-Person Interviews, and Direct Mail Questionnaires.

Our minimum test size is above 4,000 respondents, with no more than 50% coming from online responses.

We store our raw paper documents from surveys that can be accessed by major publications for verification.


Population Parameter vs. Sample Statistic
The reason for conducting a sample survey is to estimate the value of some attribute of a population.

» Population parameter. A population parameter is the true value of a population attribute.

» Sample statistic. A sample statistic is an estimate, based on sample data, of a population parameter.
Consider this example. A public opinion pollster wants to know the percentage of voters that favor a flat-rate income tax. The actual percentage of all the voters is a population parameter. The estimate of that percentage, based on sample data, is a sample statistic.

Probability vs. Non-Probability Samples
As a group, sampling methods fall into one of two categories.

» Probability samples. With probability sampling methods, each population element has a known (non-zero) chance of being chosen for the sample.

» Non-probability samples. With non-probability sampling methods, we do not know the probability that each population element will be chosen, and/or we cannot be sure that each population element has a non-zero chance of being chosen.

Non-Probability Sampling Methods
Two of the main types of non-probability sampling methods are voluntary samples and convenience samples.

» Voluntary sample. A voluntary sample is made up of people who self-select into the survey. Often, these folks have a strong interest in the main topic of the survey.

» Convenience sample. A convenience sample is made up of people who are easy to reach.

Probability Sampling Methods

The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling.

» Simple random sampling. Simple random sampling refers to any sampling method that has the following properties.

» Stratified sampling. With stratified sampling, the population is divided into groups, based on some characteristic. Then, within each group, a probability sample (often a simple random sample) is selected. In stratified sampling, the groups are called strata.

» Cluster sampling. With cluster sampling, every member of the population is assigned to one, and only one, group. Each group is called a cluster. A sample of clusters is chosen, using a probability method (often simple random sampling). Only individuals within sampled clusters are surveyed.

» Multistage sampling. With multistage sampling, we select a sample by using combinations of different sampling methods.

» Systematic random sampling. With systematic random sampling, we create a list of every member of the population. Using the list, we randomly select the first sample element from the first k elements on the population list.