Statistics are more prevalent in our day to day lives than many of us even realize. Every time we check the weather before deciding what to wear in the morning, we’re relying on statistics. When our insurance company informs us of the rates we’re going to be charged, they’ve determined those numbers based on stats of all the drivers and homeowners in our area. When a friend offers us a cigarette at a party, we may politely decline because it’s hard to ignore the statistics about cancer rates linked to smoking.

The field of statistics is the science of learning from data, and without an understanding of it, we would have a very difficult time making decisions. When preparing for emergencies, for example, we can assess the risks based on statistics. How often do hurricanes occur in this area? How likely is one to occur within the next week? When a hurricane does hit, how devastating is it expected to be? The National Hurricane Center uses various statistical models "based on historical relationships between hurricane-specific information, such as the location and time of year, and the behavior of historical hurricanes" to predict future storms. These stats are incredibly important in keeping citizens safe and minimizing destruction from hurricanes.

Statistics also play a huge role in political campaigns. We can predict the outcome of elections by using data about an area from past elections and by polling individuals during a campaign to see where they are leaning. Politicians running for office use this data to decide where to focus their energy as well. If a county has voted Republican in every presidential election for the past 20 years, they are likely to vote the same way in the future, regardless of how many rallies are held there.

Statistics have also been incredibly important to governments in combating the Covid-19 pandemic. Decisions about lockdowns, restrictions, mask mandates and vaccine requirements have all been made based on illness and death rates over time. It would have been very difficult for governments to determine a course of action in fighting Covid without proof of how it’s spreading and where.

Statistics also benefit us when we’re doing our weekly grocery shop. Stores use the data they collect on what products are being purchased the most frequently to decide how much to order. This is particularly important for items that become more popular in specific seasons, such as sunscreen in the summer months and hot chocolate in the winter. And if you want to determine the best time to go grocery shopping to avoid crowds, the stats will tell you to go on Mondays and Tuesdays. Skip shopping on Saturdays and Sundays, especially between 11am-12pm, as shoppers spend an average of 7 more minutes in stores on weekends due to crowds.

Statistics can also be exciting when learning about topics we’re passionate about, like sports. Data analytics have become extremely important in the world of professional sports for team strategies and for the enjoyment of fans. It wasn’t until 2002, however, that sports analytics really started to take off. The catalyst was Billy Beane, who was general manager of the Oakland Athletics at the time, using statistical analysis to curate a powerful team of lesser-known baseball players. After the Athletics almost won the World Series, Beane’s strategy, which came to be known as “Moneyball”, quickly became the norm for other teams as well and even inspired a 2011 film starring Brad Pitt.

According to a report from Research and Markets, the global sports analytics industry is expected to reach $3.4 billion by 2028. Part of the reason for this is the speed at which analytics technology has evolved. Companies like Genius Sports, for example, are able to “generate statistical breakdowns from video footage to help coaches optimize their play calling during games or generate post-game takeaways”. Cameras are also used by many  companies to track player movement, ball speeds and more. But it’s not only the teams who benefit from this data. Fans use these statistics to create their fantasy league teams and place bets on who will win games.

No matter what your favorite sport is, I’m sure you can find a high-tech analytics company that’s pumping out fascinating data about your favorite athletes. For example, Trace in Austin, Texas, uses recording gear and an AI system to analyze film from soccer games, so coaches and players don’t have to. Players wear tracking devices that record their games, and an AI bot stitches together clips of all of the most important moments. Trace also notes performance metrics including minutes played, distance ran, top speed, max efforts, and stamina, and creates a heat map tracking where players spent most of their time during a game. Technology like this saves coaches time and helps them create more effective strategies for future games.

When it comes to statistical analysis, there are 7 key types: descriptive, inferential, predictive, prescriptive, exploratory, causal and mechanistic. Each type serves a slightly different purpose, but they all help the world go round. Descriptive statistics, for example, deal with “organizing and summarizing data using numbers and graphs”. Using visual aids like graphs and tables is an efficient way to convey information because it's more palatable for the general population. These are often used in news articles to simplify complex data. As the name suggests, inferential statistics allow us to infer generalizations about data and “make conclusions with respect to future outcomes” by testing hypotheses. This method often uses the sampling theory, various tests of significance and statistical control.

Predictive analysis is also pretty self-explanatory, as it is used to make predictions about future events based on “current and past facts and figures". We rely on predictive analysis for weather forecasts, and many businesses including marketing companies, insurance companies, financial corporations and online service providers use predictive analysis to gain competitive advantages. Conversely, prescriptive analysis uses data to determine the best reaction to a situation. It relies on several techniques including simulation, graph analysis, algorithms, complex event processing and more. 

Exploratory analysis is generally “the first step of the data analysis process that is conducted prior to any other statistical analysis techniques”. It is not the sole method for predicting or drawing conclusions, but it provides a preview of data and can give valuable insight. Causal analysis is used to understand the reason behind why events occur. For example, it can be used by businesses to trace what went wrong in a failed experiment or by IT companies to check the quality assurance of their software. Lastly, mechanistic analysis is the least common type and is used to “understand the definite changes that could make changes in other variables”. For example, it is used in biological sciences "when studying and inspecting how various parts of the virus are affected by making changes in medicine".