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investigators almost always test an equal number of animals in each condition, and in each combination of conditions Although analyses can be conducted of data with unequal numbers of animals in each cell, or even missing cells, they require some additional assumptions; in rare cases an investigator may choose to use a larger number of animals in a condition likely to produce more variable data If only a single animal is used in each combination of conditions, there can be no separation between interaction and random error If two or more animals are used in each combination of conditions, a measure of variability within cells can be calculated Ideally, investigators would determine the number of animals to be tested in each condition based on an analysis of power.

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To do this the investigator makes an estimate of the within-group variability to be expected and an estimate of the expected size of the effect These estimates are typically based on previous research Alternatively, the investigator may decide on the size of the effect that is worth identifying With this information and some assumptions about the form of the error distribution, the investigator can rationally decide on the number of animals per group In fact, most investigators do not do a power analysis (or do not report doing so) in deciding on the number of animals per group Many undoubtedly use the number of animals typically used in similar experiments If there are many such similar experiments, this approach may be satisfactory because previous investigators will have found by trial and error a number of animals per condition that is suf cient to identify phenomena (ie.

To help make it easier, the CD-ROM in the back of this book has several standard style sheets on it. Pick one that comes close to approximating the look you want and customize it to make it your own.

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There are a number of important factors that will in uence the design of a local network. Among these factors are:

Figure 13.1 shows a typical deployment of the COOP environment. In this example, there is a single client subsystem instance ( Client-A ) deployed at Site A, a single distributed query service provider (DQSP) subsystem instance ( DQSP-B ) deployed at Site B, and a single data dictionary service provider (DDSP) subsystem instance ( DDSP-C ) with its associated global interface schema (GIS) instance deployed at Site C. Sites D and E are identical with respect to the number of component and subsystem instances deployed at each site at each site there is a single local query service provider (LQSP) subsystem instance deployed along with a single instance of a local deployment schema (LDS) for that site. At each site, there is also a single instance of a Sub-DBE (S-DBE), which we refer to as a local database environment (LDBE), deployed along with the local databases (LDBs) that it contains and controls. This is merely a simple example deployment: an actual deployment in the real world could have many more clients, service providers, and DBEs, but this diagram should be suf cient for our discussions in this chapter. The subsystem instances in the COOP database environment can be heterogeneous or homogenous. For example, LDBE-D might be a relational database management system (RDBMS) while LDBE-E could be either the same RDBMS package, a different RDBMS package, or a non-RDBMS based DBE. Usually, the DBEs will depend on existing system deployment details while the various service provider subsystems

, a low Type II error rate), but not many more than necessary (ie, a number of animals that produces an exceedingly small p value) The number of animals in repeated-measures designs is based on similar considerations In the single-subject designs, however, the decisions are based almost entirely on convention The term single subject refers to the fact that separate analyses are done of each animal, not that the experiment consists of only one animal Because of concern that the behavior of a single animal may not be representative of others, it is typical to use three or ve animals Ideally, all of them will show similar patterns of results.

Editing information property list keys is a great way to learn about how applications work with the Finder and the rest of Mac OS X, but it s also an efficient technique for making your application unusable. To be safe, you should make a copy of your application before fooling around with it.

In cases in which the performances of the animals differ, the investigator may comment on the range of possible behaviors of different animals, and because an odd number of animals are typically tested, the investigator may also comment on the typical pattern In experiments on animal learning, the investigator typically chooses a number of trials of training suf cient to produce relatively complete learning This is referred to as asymptotic, steady-state, or stable behavior This number may be chosen before the beginning of the experiment based on the number used in similar experiments, or it may be.

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