TABLE 7
Population Estimates and Confidence Intervals
| Tribe | Fishing-mile Estimate | Area Estimate |
| Kato | 1,523 ± 267 | 1,470 ± 263 |
| Bear River | 1,276 ± 353 | 840 ± 556 |
| Lassik | 1,411 ± 300 | 2,020 ± 291 |
| Nongatl | 2,325 ± 462 | 2,830 ± 692 |
| Shelter Cove Sinkyone | 2,145 ± 374 | 1,920 ± 257 |
The question of whether the fishing-mile estimates yield shorter confidence intervals than the area estimates brings up an entire range of problems pertaining to economy, settlement pattern, and the like. The obvious interpretation of the shorter confidence intervals would be that the economy of the people in question depended more on fish and fishing than on the general produce over the whole range of their territory. The question then becomes one of quantitative expression—we would like to have some index of the extent of dependence on various factors in the economy. This might best be approached from the standpoint of analysis of covariance, where we would obtain the "components of variance." This technique is a combination of the methods of regression used in this paper and those of the analysis of variance. It would evidently yield sound indices of economic components, but it involves, for myself at least, certain problems of calculation and interpretation which will have to be resolved in the future.
Another problem of this kind turns on the question of which factors are important in which area. Considering the State of California, for instance, we might want to know about such factors as deer population, water supply, the quantity of oak trees, etc. Any one of these factors or any combination of them might be important in a particular area; the problem of gathering the pertinent information then becomes crucial. Moreover, because the situation has changed since aboriginal times, we must combine modern information with available historic sources. S. F. Cook has shown that energetic and imaginative use of these sources yields very good results (e.g., Cook, 1955).
Finally, there is the problem of the assumptions we were required to make in order to obtain our population estimates. Although many of the assumptions in the present paper are difficult to assess, the two which I would like to discuss here were particularly unyielding—the assumptions of the number of persons per house and the assumptions of the number of houses per village.
The question of how many persons there were per house has been dealt with extensively by both Kroeber and Cook. There is also a great deal of random information in the ethnographic and historical literature. I believe there are enough data now at hand to provide realistic limits within which we could work, at least for the State of California. This information should be assembled and put into concise and systematic form so that it would be available for use in each area. It would also be of interest in itself from the standpoint of social anthropology.
For the number of houses per village we have also a considerable body of information, but here we are faced with a slightly different problem. It often happens that we know, from ethnographic information or from archaeological reconnaissance, how many house pits there are in a village site but do not know how many of the houses which these pits represent were occupied simultaneously. In the present paper it has been assumed that four-fifths of the house pits represents the number of houses in the village occupied at any one time. This, however, is simply a guess, and one has no way of knowing how accurate a guess. The solution to this problem is simple but laborious. From each area of the State a random sample of villages with recorded house counts should be taken. Each of these village sites should then be visited and the house pits counted. A comparison of the two sets of figures would give us a perfectly adequate estimate, which could then be used subsequently over the entire area.