Empirical studies in psychology typically employ null hypothesis significance testing to draw statistical inferences. We propose that likelihood ratios are a more straightforward alternative to this approach. Likelihood ratios provide a measure of the fit of two competing models; the statistic represents a direct comparison of the relative likelihood of the data, given the best fit of the two models. Likelihood ratios offer an intuitive, easily interpretable statistic that allows the researcher great flexibility in framing empirical arguments. In support of this position, we report the results of a survey of empirical articles in psychology, in which the common uses of statistics by empirical psychologists is examined. From the results of this survey, we show that likelihood ratios are able to serve all the important statistical needs of researchers in empirical psychology in a format that is more straightforward and easier to interpret than traditional inferential statistics.