Data scientists collect, analyze, distribute, and interpret data from a wide variety of sources. One of those sources is the field of statistics.

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### Bayesian Thinking

One area many data scientists focus on is Bayesian thinking. This is where a business model changes as more and more data and statistics are gathered. It’s actually a key function of machine learning as used within various business models. When businesses incorporate Bayesian thinking in their organization, it provides certain flexibility not previously developed within the business plan.

### Probability Distribution

Data scientists use statistics when working with probability distribution. Data scientists use probability on a scale between 0 and 1 for an event to happen, with 0 meaning there is no chance of that event occurring and 1 meaning it will certainly happen. Using statistics, as well as raw data from other sources, data scientists can constantly update probability distribution numbers in order to keep the probability number as current as possible.

### Skepticism

There appears to be some skepticism as to whether data science is all that different from statistics. Some experts believe that statisticians have simply created the term “data science” to give the profession a more contemporary-sounding marketing appeal. Either way, there is little doubt statistics forms an integral part of data science.

### Differences

One key difference between data science and statistics is that statistics are geared towards predicting probability while data science simply gathers all available data and presents it to organizations and individuals.

### Machine Learning

One important way data scientists use statistics is in the development of machine learning, which is also known as “artificial intelligence.” A data scientist takes the numbers found within various sets of statistics and uses them within algorithms to aid a company or an organization in the decision-making process.

### Insurance Software

Data scientists use statistics when developing software for the insurance industry. A data scientist can take the statistics for any variable and add those numbers to other data gathered. While statistics might show that cars that are bright red are more likely to be caught speeding than any other color, data scientists can delve deeper into the statistics to find out whether the age of the driver or the location of the violation might also have something to do with receiving a ticket.

### Drawbacks

One of the drawbacks for data scientists today is the inability to gather statistical data on their own, without needing the benefit of big data. Due to the incredible amount of sheer data available, data scientists are forced to rely upon software programs to give them needed information. As *Forbes* pointed out, however, this also means a great many data scientists are unable to actually “look under the hood” of the algorithmic programs they are using.

While statistics might no longer be the only tool that data scientists have available in today’s technology marketplace, they still play an important role in the development of new software, new marketing techniques, and in creating a business plan for a company or an individual.