Let's start with a scenario: Suppose the platform Whatsapp Database sells two mobile phones A and B. 800 people like mobile phone A and 200 people dislike it; mobile phone B has 9 people like it and 2 people dislike it. So, which phone do users prefer? I believe that this scene Whatsapp Database has been encountered by all my friends in daily life and work. How do you usually make judgments? I hope that through today's article, I can give you a new perspective and a more scientific solution.
A common measurement method I think Whatsapp Database everyone's first reaction should be to measure according to the ratio? therefore, A mobile phone preference rate=800÷(800+200)=80% B mobile phone preference rate=9÷(9+2)=82%80%<82% Therefore, users Whatsapp Database prefer B mobile phones. is this correct? It looks fine. After all, the higher the like rate, the more users like it! However, I believe my friends can also see the clues of this example: the total sample size of mobile phone B is only 11.
Although the like rate is high, the sample Whatsapp Database size is so low that any data change will have a huge impact on the results. Therefore, according to this ratio method, is the calculated like rate "reliable"? In statistical language, confidence? 2. Wilson's score Whatsapp Database Above we feel that it is a bit difficult to measure according to the simple like rate. But if you don't compare by like rate, how else can you calculate it? That's our topic today: Wilson's score. 1. Formula Definition Let’s first look at the specific Wilson score calculation formula: