The authors develop a sure independence screening method based on hypothesis testing (HT-SIS) in a general nonparametric regression model. \textit{J. Fan} and \textit{J. Lv} [``Sure independence screening for ultrahigh dimensional feature space (with discussion)'', J. Royal Stat. Soc. B 70, No. 5, 849--911 (2008; \url{doi:10.1111/j.1467-9868.2008.00674.x})] introduced the concept of sure independence screening, that is, with probability tending to one, a well chosen subset of the predictors will contain the true set of predictors that contribute to the underlying model. The ranking utility is based on a test statistic for the hypothesis predictive significance of each available covariate. The authors establish the sure screening property showing that all active predictors are retained with high probability as the sample size increases. The threshold parameter is chosen based on the desired false positive selection rate. Some simulation results and an analysis of a real data set are presented.