For which scenario is the Durbin-Watson test generally considered inconclusive?

The Durbin-Watson test is specifically designed to detect the presence of autocorrelation in the residuals from a regression analysis. When sample sizes are small, the test's inability to provide definitive results is due to the limited data reflecting the underlying patterns in the errors. With small sample sizes, the variability in the computed Durbin-Watson statistic can lead to less reliable conclusions regarding autocorrelation. This is because the test relies on the calculation of the statistic being affected by the distribution and number of observations; smaller samples can lead to misleading interpretations since there may not be enough data points to reveal a consistent pattern.

In contrast, larger sample sizes provide a more robust dataset, which improves the reliability of the conclusions drawn from the Durbin-Watson statistic. The test becomes more meaningful with additional data, as it can better capture the overall behavior of the residuals. Therefore, the uncertainty and inconclusiveness primarily arise when dealing with small samples.