First, the table contains two datapoints - high gun states and low gun states. It so happens that looking at the extremes of gun ownership, states with more guns have more homicides and suicides. So let's not stop there, let us break out the data and look at individual states, that will then give us a total of 17 data points. If mikeb's hypothesis is correct, then we should see an increasing trend among the high gun states (as % ownership goes up), and a decreasing trend among low gun states (as the % ownership goes down).
|2006 Death Rate of Women (per 100,000) High Gun States|
For high gun states, we don't see a correlation (trend) between gun ownership and homicides. Same for suicides and same for accidents. So lets look at low gun states:
|2006 Death Rate of Women (per 100,000) Low Gun States|
Again, same result, there is no correlation(trend). Even if we ignore DC, there is still no correlation. So the hypothesis fails when more than two data points are looked at (using the exact same set of data).
Comparing the two, the low gun states in general have lower homicide rates than the high gun states. Except for SD and ND which have lower rates than all of the low gun states. And MT is lower than all but 1 low gun state. Interestingly enough, SD, ND and MT are contiguous to each other. Which leads us to the first major problem with Hemenway's table: Simplified conclusions (such as mikeb's) only work if all other variables are equal. In this case they are clearly not.
Hemenway and mikb make the mistake of using only a 2 datapoint set to formulate a conclusion. With only 2 datapoints, no outliers or exceptions are identified. Once the same data to make the 2 datapoints is taken back to its original 17 datapoints, at least 4 outliers (the 3 mentioned above and DC) begin to emerge for homicides alone. Whenever one is examining a dataset, you cannot ignore the outliers. You may decide not to include them in your dataset (DC) but you must provide a valid reason for the expulsion. By crunching the numbers down to only 2 datapoints, Hemenway and mikeb cover up the datapoints that go contrary to their hypothesis.
The United States is not a homogenous society. I complain about California drivers and Boston drivers. I have never complained about Oklahoma or Texas drivers. Rhode Island and Utah have very homogeneous religious makeups. Most every other state does not. North Dakotans don't have problems with punch card ballots. Floridians do. The simple fact is, people (in general) act differently depending on where they live. Even within a state, people are different in different counties (like the Texans who can't stand people from Dallas). So to simplify an issue like homicide and guns in the whole nation and assume that gun ownership is the major factor contributing to it is dishonest. Looking at the datapoints I see three distinct regions that have very similar numbers: 1) the previously mentioned ND, SD, MT and I'll add in WY, make a contiguous region that has firearm homicide less than 0.8. 2) AL and MS are next to each other and have very similar homicide rates. 3) MA, CT, RI, and NJ are all right next to each other and while MA firearm homicide rate is about 1/3 the rate of the other three states, the non-firearm homicide rate is very similar. So, 10 of 17 of the data points fall rather neatly into a regional category. Perhaps then this has more of an influence than firearm ownership? I'll have to look at that.
So to sum up what I have looked at, the numbers for the states that make up the high gun and low gun states do not mesh well with the hypothesis of more guns = more homicides, suicides, and accidents. There is no trend. In general one can conclude that there are more firearm homicides, suicides, and accidents in the states that have the most guns compared to the states that have the least guns. But this does not mean that guns are the cause (if that were the case we should be seeing a trend).