When examining data, such as the grades earned by 1,000 students for a course, it is possible to use both descriptive and inferential statistics in your review of their grades. Normally, in almost all research conducted on groups of people, you will use descriptive and inferential statistics to examine your results and draw conclusions. So, what are descriptive and inferential statistics and what are their differences? Descriptive statistics is the term given to the examination of data which allows data to be described, shown or examined in a meaningful way, so that, for example, shapes can emerge from the data. Descriptive statistics, however, do not allow us to draw conclusions beyond the data we examined or draw conclusions about hypotheses we might have made. They are simply a way to explain our data. Descriptive statistics are very important because if we only presented our raw data, it would be difficult to imagine what the data shows, especially if there is a lot of it. Descriptive statistics thus allow us to present the data in a more meaningful way, which allows for a simpler explanation of the data. For example, if we had the results of 1,000 student course assignments, we might be fascinated by the overall performance of those students. We would also be interested in dividing or distributing the brands. Descriptive statistics allow us to do this. How to properly describe data using statistics and graphs is an important topic and covered in other Lard Statistics guides. Usually, two common types of statistics are used to explain data: measures of central tendency and measures of spread. The measurement of the central tendency of these