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the point of statistics is to inform some decision-making process. An estimate of the region in which bombs are likely to fall would directly inform your decis

the point of statistics is to inform some decision-making process. An estimate of the region in which bombs are likely to fall would directly inform your decision-making process ("Don't go over there!") so I don't understand your objection.

lisper 14y ago
I don't know what shardling intended, but here's my take on it: the kind of estimate you want depends on the question you want to answer. I live in California, so the analysis I'm doing on the rockets coming out of Gaza is likely very different from the one being performed by the people living in Tel Aviv. And both of those analyses are different from the ones being performed by Hamas. So there is no such thing as a reliable "estimate of the region in which bombs are likely to fall" independent of the particular question you want to answer. Another example, from the original article: "a weather forecaster is good if it rains 95 percent of the times he says there is a 95 percent chance of rain" It's not clear whether or not there's something special about the number 95, or whether the intent is that a forecaster is good if it rains X% of the time he says there's an X% chance of rain for any X. So consider a 100 day period during which it rains 10 days, and a forecaster who every day predicts a 10% chance of rain. Is that a "good" forecast? If you're a farmer, it might be. If you're planning a picnic, not so much.
shardling 14y ago
Because boiling everything down to a simple model like "regions where bombs are likely" is throwing away information. We do so because it's easier to analyze and think about a simple model than to try to deal with the whole dataset all at once -- except that if you have a specific question, it might be possible to treat that question directly. Because you're not losing information, doing so provides a more accurate result. In practice this might not matter, and simplified models might be good enough. But as computers become more powerful, it becomes easier to query the data directly.

lisper 14y ago

I don't know what shardling intended, but here's my take on it: the kind of estimate you want depends on the question you want to answer. I live in California, so the analysis I'm doing on the rockets coming out of Gaza is likely very different from the one being performed by the people living in Tel Aviv. And both of those analyses are different from the ones being performed by Hamas. So there is no such thing as a reliable "estimate of the region in which bombs are likely to fall" independent of the particular question you want to answer. Another example, from the original article: "a weather forecaster is good if it rains 95 percent of the times he says there is a 95 percent chance of rain" It's not clear whether or not there's something special about the number 95, or whether the intent is that a forecaster is good if it rains X% of the time he says there's an X% chance of rain for any X. So consider a 100 day period during which it rains 10 days, and a forecaster who every day predicts a 10% chance of rain. Is that a "good" forecast? If you're a farmer, it might be. If you're planning a picnic, not so much.

shardling 14y ago

Because boiling everything down to a simple model like "regions where bombs are likely" is throwing away information. We do so because it's easier to analyze and think about a simple model than to try to deal with the whole dataset all at once -- except that if you have a specific question, it might be possible to treat that question directly. Because you're not losing information, doing so provides a more accurate result. In practice this might not matter, and simplified models might be good enough. But as computers become more powerful, it becomes easier to query the data directly.