### Reasons Why We Study Statistics

The statistics course is a science of learning from data. The knowledge gained after this course helps one apply proper data collection methods and make correct analyses for correct results. Statistics is an important process behind scientific discoveries and how predictions and decisions are based on the presented data. It is through statistics that you can understand a subject deeper. Statistics has many units, including inferential statistics, descriptive and statistical modelling. And it can be used in various fields like healthcare, government, science and business. So why is it important for you to study statistics?

1.      To understand the world better

Descriptive statistics such as summary statistics, charts and tables are used in describing raw data. All these help one to better understand existing data. For instance, if you have a set of raw data with 50000 student scores in a certain state, you can: generate a histogram to visualize the distribution of their scores. You can also find the mean score and standard diversion of their scores, or you can create a frequency table to better understand the distribution of their scores. Descriptive statistics helps us to understand the scores much better than having only the raw data.

2.      To make future predictions

Mastery of statistics helps us understand basic regression models like logic, simple, and multiple linear regressions. These models help one make predictions of the response variable based on the value of specific predictor variables in the model. For instance, businesses use multiple linear regression models when using predictor variables such as income, age, ethnicity, and more to predict how much customers will spend in their businesses. In the same breath, logistics companies use predictor variables such as population, size and total demand to predict future sales. In every industry, chances are high that you will apply regression models to predict future phenomena.

3.      To be wary of potential bias in studies

We study statistics courses to be wary of various types of bias in real-world studies. Examples of bias include:

• Observer bias.
• Referral bias.
• Self-selection bias.
• Non-response bias.
• Omitted variable bias
• Undercoverage bias.

Understanding these potential biases allows you to avoid them when conducting research. Equally, you can be aware of them when studying other people's research studies or papers.

4.      To understand assumptions made by statistical tests

Various statistical tests make assumptions concerning the underlying data under study. When performing a study or reading the results of one, it is crucial to understand the right assumptions to make the results reliable. Some assumptions used in statistical tests include the assumption of equal variance, the assumption of normality, and the assumption of independence in statistics.

5.      Help to analyze data

Statistics assists in data analysis as it collaborates with fields like applied mathematics. It also assists in the packaging, abstraction and comprehension of the topic areas. The creative use of computing systems has made research in information science a breeze. To perform the duties of a statistician, you must have background knowledge of statistics.