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Statistical Data


                  When machines have to process a large amount
                  of numerical data, Statistical Data enables them to
                  understand, analyse, and give output in the form
                  of meaningful information.
                  You can understand it in this way: when you need

                  to know the average age of a town, the data
                  would consist of huge numbers, but numerical
                  calculation would be easy. However, if you want to
                  see how the average life expectancy of the people
                  in the town changes over time, statistical data
                  processing would help you to predict the average age of healthy individuals. Let’s understand
                  it with some more examples.

                     •  If you show an  AI application  many pictures of camels and  giraffes,  it uses statistics to
                      determine what makes a camel look different from a giraffe.

                     •  Based on history, AI can predict potential future outcomes. Like, YouTube recommends video
                      based on your previous viewing preferences.
                     •  Statistical methods help AI identify its mistakes and learn how to fix them. This helps the
                      model to improve over time.


                  Why is Statistical Data Important?

                  Statistical data is essential in any AI system, as it helps uncover hidden and unexpected
                  insights. Its visual representation makes the data easier to understand, while data analysis
                  supports informed decision-making. The following points highlight the importance of
                  statistical data in AI:
                     •  AI uses statistics to analyse data, extract patterns, and make predictions.

                     •  Statistical techniques help forecast outcomes using past data.

                     •  Statistics quantifies uncertainty, aiding accurate decisions.
                     •  Different metrics like precision, recall, and F1-score assess model performance.
                     •  It helps AI understand cause-effect relationships for better predictions.

                     •  It identifies trends, outliers, and meaningful patterns in data.

                     •  It uses statistical methods to learn, reason, and predict.


                          Real-Life Application of Statistical Data


                  Statistical data is all around us and helps in making smart decisions every day. Let’s learn some
                  real-life applications of Statistical Data.



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