Privacy Policy This privacy policy applies to the Statistic Distributions CDF app (hereby referred to as "Application") for mobile devices that was created by Misbah Aiad (hereby referred to as "Service Provider") as a Free service. This service is intended for use "AS IS". Information Collection and Use The Application does NOT collect any type of information, neither online (account based) nor offline (user device). The Application does not connect to the internet at all, nor stores anything entered by the user on their device. Third Party Access Only aggregated, anonymized data is periodically transmitted to external services to aid the Service Provider in improving the Application and their service. The Service Provider may share your information with third parties in the ways that are described in this privacy statement. Please note that the Application utilizes third-party services that have their own Privacy Policy about handling data. Below are t...
Anytime you aim to perform a study on the entire population, you will surely find that this task will be: Much time and/or efforts consuming as populations are normally huge . Impossible if the population is infinite (such as products). Here comes the role of taking samples. Yes! we just take a sample from the whole population, perform the study on the chosen sample, apply the results back to our population. This is the core of inferential statistics because what we do is to infer parameters/properties of the population using information from a small sample. Well, this does not mean we will obtain 100% exact accurate estimations or inferences. But to be as close as possible, sample elements should be taken randomly ! At least, being random in sample selection will mostly include the diversity of information/facts within our population.
Well, no fixed criteria in statistics. Yes, there exist many theories and methodologies to create random sequences or numbers. However, this will depend on the population nature, situations and surrounding environment. In general, we may think like following: Many persons/machines to share in sample selection is better than to be done by only one. Diversity leads to better randomness. Selecting in different times/situations/places is better than to do at once in order to get more randomness. Changing methodologies/media of selection may help. Combining two or more random samples create better random sample. Anyway, since our goal is to get accurate inferences, we should try as possible to be randomized in sample selection.
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