Coincidence that is statistically impossible seems to us like an irrational event, and some define it as a miracle., But, as Montaigne has said, "the origin of a miracle is in our ignorance, at the level of our knowledge of nature, and not in the nature itself.", Glourious miracles have been laternon discovered to be obedience to the laws of nature or a technological development that was not widely known at the time., As the German poet, Goethe, phrased it: “Things that are mysterious are not yet miracles.” , The miracle assumes the intervention of a “higher power” in its occurrence that is beyond human capability to grasp. . , Yet there are methodical and simple ways to “cause a miracle” without divine revelation and inspiration. , Instead of checking it out, investigating and finding the source of the event, we define it as a miracle. , The miracle, then, is the excuse of those who are too lazy to think.

Information encountered after an event can influence subsequent remembering. , External information can easily integrate into a witness’s memory, especially if the event was poorly encoded or the memory is from a distant event, in which case time and forgetting have degraded the original memory. , With reduced information available in memory with which to confirm the validity of post-event misinformation, it is less likely that this new information will be rejected. , Instead, especially when it fits the witness’s current thinking and can be used to create a story that makes sense to him or her, it may be integrated as part of the original experience. , This process can be explicit (i.e., the witness knows it is happening), but it is often unconscious. , That is, the witness might find himself or herself thinking about the event differently without awareness. , Over time, the witness may not even know the source of information that led to the (new) memory. , Sources of misinformation in forensic contexts can be encountered anywhere, from discussions with other witnesses to social media searches to multiple interviews with investigators or other legal professionals, and even in court.

Correlations are powerful because the insights they offer are relatively clear. , These insights are often covered up when we bring causality back into the picture. , For instance, a used-car dealer supplied data to statisticians to predict which of the vehicles available for purchase at an auction were likely to have problems. , A correlation analysis showed that orange-colored cars were far less likely to have defects. , Even as we read this, we already think about why it might be so: Are orange-colored car owners likely to be car enthusiasts and take better care of their vehicles? Or, is it because orange-colored cars are more noticeable on the road and therefore less likely to be in accidents, so they’re in better condition when resold? , Quickly we are caught in a web of competing causal hypotheses. , But our attempts to illuminate things this way only make them cloudier. , Correlations exist; we can show them mathematically. We can’t easily do the same for causal links. , So we would do well to hold off from trying to explain the reason behind the correlations .

But much as converting a digital image or song into a smaller file results in loss of data, information is lost when sampling. , Having the full (or close to the full) dataset provides a lot more freedom to explore, to look at the data from different angles or to look closer at certain aspects of it. , (C) A fitting example may be the light-field camera, which captures not just a single plane of light, as with conventional cameras, but rays from the entire light field, some 11 million of them. , The photographers can decide later which element of an image to focus on in the digital file. , There is no need to focus at the beginning, since collecting all the information makes it possible to do that afterwards. , Because rays from the entire light field are included, it is closer to all the data. , As a result, the information is more “reuseable” than ordinary pictures, where the photographer has to decide what to focus on before she presses the shutter.

By the nineteenth century, France had developed a system of precisely defined units of measurement to capture space, time, and more, and had begun to get other nations to adopt the same standards. (, ① ) Just half a century later, in the 1920s, the discoveries of quantum mechanics forever destroyed the dream of comprehensive and perfect measurement. (, ② ) And yet, outside a relatively small circle of physicists, the mindset of humankind’s drive to flawlessly measure continued among engineers and scientists. , ( ③ ) In the world of business it even expanded, as the precision-oriented sciences of mathematics and statistics began to influence all areas of commerce. (, However, contrary to the trend of the past several decades, in many new situations that are occurring today, allowing for imprecision — for messiness — may be a positive feature, not a shortcoming. , As a tradeoff for relaxing the standards of allowable errors, one can get a hold of much more data. ( , ⑤ ) It isn’t just that “more is better than some,” but that, in fact, sometimes “more is greater than better.”