  The abundance of new cosmological data becoming available means that a wider
range of cosmological models are testable than ever before. However, an
important distinction must be made between parameter fitting and model
selection. While parameter fitting simply determines how well a model fits the
data, model selection statistics, such as the Bayesian Evidence, are now
necessary to choose between these different models, and in particular to assess
the need for new parameters. We implement a new evidence algorithm known as
nested sampling, which combines accuracy, generality of application and
computational feasibility, and apply it to some cosmological datasets and
models. We find that a five-parameter model with Harrison-Zel'dovich initial
spectrum is currently preferred.
