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In: Business and Management

Submitted By songyexue
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Description When I was come back to China this summer, something bad happened to me. My boyfriend and I went to Beijing to meet a friend and we supposed to get to a popular shopping mall by crowded subway. Arriving our destination, we get out of the subway and walk along the sidewalks, talking and laughing, because there was no more crowded and I just release my attention to the bag. Just about one second, I feel my bag become lighter and I touch the zip on bag fast and I found that the bag was open! I quickly found that my boyfriend’s wallet was stolen and the whole process just kept for 1 minute. There are two sinkiang people came to us and told us that they witnessed a woman steal our wallet and my cell phone and then throw my cell phone in the flowerbed and then that woman was running away and one of the sinkiang people point out a direction. My boyfriend was angry and just ran to that direction and didn’t find any clues. When we calmed down, we thought that maybe those two sinkiang people did that and lied to us to avoid any suspect. My boyfriend ran to them and yelled to them: “Give me back the wallet!” However, they didn’t admit that they have our wallet and just go away.
Explanation 1 (causal)
There are two most plausible explanations in this event about who actually stolen my boyfriend’s wallet. The first explanation for the theft of the wallet is that an unknown woman opened my bag without my noticing and took the wallet. This could be true because the skills of thieves stealing things are really good nowadays and they live on this for their career. Subway must be the most frequently used transportation in Beijing. The most passengers in subway are people who don’t have cars and got less salary and they go almost everywhere by subway because it is cheap and convenient. There are varieties kinds of people no matter what educational level they have or no matter how much salary they earn each month. Therefore, it is not easy to distinguish who is good or not, and there are lots of thieves hiding in people. The second possible explaination is that the thieves are exactly those two sinkiang people. There is a popular phenomenon in China which is that sinkiang people is good at stealing things because almost all the thieves are sinkiang people. Sinkiang is a province in China and sinkiang is a nationality in China among all 56 nationalities. The reason why sinkiang people like stealing things is that this nationality is not rich and this province is one of the poor provinces in China. Poor family can’t support their children to get a higher education. Therefore, poor people always look for a job in prosperous cities and want to stand stable in prosperous part of the society. Since they are poor, they can’t do a big investment to make a great business, like selling things and due to their poor educational level, they can’t find a god job in any company. Their whole property even cannot make them finishing hungry everyday. Therefore, most of the sinkiang people didn’t find their position in this city, so most of them choose to steal things. At that situation, sinkiang people have the most possibility to steal things and I didn’t find any sinkiang people except those two around us and I remember they came from my back. The possible situation is that they lied to us because they wanted to eliminate their suspect and made us believe that other person stolen the wallet and let us chase her so that they can easily run away.
Explanation 2 (statistical) For the first possibility, there are about 20 million 186 thousand population in Beijing, and the number of people who use subway is 5million 100 thousand everyday, so the percentage that a random person in subway station steal the wallet could be 25%. We suppose that woman was 50% of the people who use subway; so the random woman stole the wallet could be 12.5%. For second possibility, there are about 19 million 610 thousand people who are registered permanent residence in Beijing and external population from the other parts of China in Beijing is about 9million 92 thousand according to population census in 2010. What’s more, minority in Beijing is about 480 thousand people including sinkiang people. However, comparing with the other parts of China, sinkiang is one of the poor provinces in China whose average income was almost at the bottom of the rank list. Income exactly influences the crime rate in China by researches. By searching on the Internet about “sinkiang people stole things”, there has about thousands of hits in which people talked about their experience about how their things was stolen by sinkiang people. These data indicates that those two sinkiang people had the great possibility to steal my boyfriend’s wallet. Besides these, my boyfriend and I didn’t see anyone running away, and this largely decrease the possibility that what those two sinkiang people said was true. As a matter of fact, those two people told us this story increase the possibility that they were lying to us and they were trying to cover up their crime.
Part 2
By comparison of causal explanation and statistical relevance explanation, I think statistical relevance explanation is more powerful because the statistical data is what people and experts got from millions of surveys and experiments and is more reliable. “For causal explanation, the cause of an event or phenomenon is the explanation of the event or phenomenon, but for statistical relevance explanation, a fact or statistical law will serve as part of the explanation for an event if it affects the probability that the event would occur.” (Causal asymmetry and explanation) Causal explanation is narrower because it described as a cause that must result in an effect. “The simplest form is that a single cause leads to a single effect: pinching your skin causes you to feel pain, lightning causes thunder.” (Causal asymmetry and explanation) However, statistical relevance explanation states a numerical data that shows the possibilities to an event. “According to the explanation model called the statistical relevance model, a fact or statistical law will serve as part of the explanation for an event if it affects the probability that the event would occur.”(Causal asymmetry and explanation)
Not all the events are must-happen events regarding to some reasons in our life except scientific events and natural phenomena, so for most events happen around our life, especially happen between persons should be uncertain events. For example, sun rises from the east and down at the west, which is a natural phenomenon that is a must-happen event because of the rotation of the earth. My personal experience that described above is an instance of uncertain events because there are several different possibilities by which how this event happened. Therefore, statistical relevance explanation is the most suitable explanation method that is more persuasive to readers.
For the first possibility in my causal explanation, a woman stolen my boyfriend’s wallet, there could be any woman at that subway station at that moment in Beijing that stole the wallet. In causal explanation, if it was a truth, then counterfactually, those two sinkiang people told a lie. “The meanings of these counterfactuals are stated correctly, but the way we use causal explanation makes them unimportant.” (Causal asymmetry and explanation) However, in statistical relevance explanation, I can get the specific numerical data about how much possibility that exist in this hypothesis. “There are about 20 million 186 thousand population in Beijing, and the number of people who use subway is 5million 100 thousand everyday, so the percentage that a random person in subway station steal the wallet could be 25%. We suppose that woman was 50% of the people who use subway; so the random woman stole the wallet could be 12.5%.” (Explanation 2) Statistical relevance explanation makes the hypothesis clearer to analyze the event.
For the second possibility, which is the most possible result in my opinion. In causal explanation, I stated a phenomenon in China that is sinkiang people always steal things and it should be like a saying among Chinese people but not an official alert. Poor people always look for a job in prosperous cities and want to stand stable in prosperous part of the society. “However, since they are poor, they can’t do a big investment to make a great business, like selling things and due to their poor educational level, they can’t find a good job in any company. Therefore, most of the sinkiang people didn’t find their position in this city, so most of them choose to steal things.” (Explanation 1) So who will believe that sinkiang people always steal things except those who had the experience that was stolen by sinkiang people? It will be persuasive, if I give some statistical data by which we can infer sinkiang people always steal things. I stated how many registered permanent residence and how much external population form other parts of China in Beijing. Sinkiang people are external population and should be a little part of population constitution in Beijing. However, by search on Internet, reviews or experience about singkiang people steal things could be hundreds of to thousands of hits. We can see through that there are a great percentage of thieves in sinkiang people. “Besides these, it makes the possibility of what sinkiang people said was true decreased due to the facts, which is that, my boyfriend and I didn’t see anyone running away.” (Explanation 2) Therefore, my hypothesis that those sinkiang people stole my boyfriend’s wallet and told a lie is probably true. The statistical relevance explanation is more reliable.
In my view, the major difference between causal explanation and statistical relevance explanation is that causal explanation relies on opinion but statistical relevance explanation relies on facts. Causal explanations may lead to arguments for some reason but statistical relevance explanation can’t because it has lots of numerical data that relies on surveys or experiment. I think statistical relevance explanation is more useful and persuasive.…...

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