Big data brings us three subversive conceptual changes: all data, not random sampling; general direction, not precise guidance; correlation, not causation. A. Not random samples, but all data: In the era of big data, we can analyze more data, and sometimes even process all data related to a particular phenomenon, instead of relying on random sampling (random sampling, We used to take this as a natural limitation, but high-performance digital technology has made us realize that this is actually an artificial limitation); B. Not precision, but confusion: so much research data So much so that we are no longer obsessed with precision; previously there was little data to analyze, so we had to quantify our records as precisely as possible, and as we scale, the obsession with precision diminishes; with big data , we no longer need to get to the bottom of a phenomenon, as long as we grasp the general direction of development, appropriately ignoring the accuracy at the micro level will allow us to have better insight at the macro level; C. It is not causality, but Correlation: We are no longer keen on finding causality, which has been a long-standing habit of human beings. In the era of big data, we no longer need to focus on the causal relationship between things, but should look for the correlation between things; correlation A relationship may not tell us exactly why something happened, but it reminds us that it is happening.
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