Editing Sampling (statistics) (section)

==Applications of sampling==
Sampling enables the selection of right data points from within the larger data set to estimate the characteristics of the whole population. For example, there are about 600 million tweets produced every day. It is not necessary to look at all of them to determine the topics that are discussed during the day, nor is it necessary to look at all the tweets to determine the sentiment on each of the topics. A theoretical formulation for sampling Twitter data has been developed.<ref>{{cite conference | author=Deepan Palguna |author2=Vikas Joshi |author3=Venkatesan Chakaravarthy |author4=Ravi Kothari |author5=L. V. Subramaniam | title=Analysis of Sampling Algorithms for Twitter | journal=[[International Joint Conference on Artificial Intelligence]] | year=2015 }}</ref>

In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage, and controller data are available at short time intervals. To predict down-time it may not be necessary to look at all the data but a sample may be sufficient.