We shouldn't be surprised if 1 of 20 predictors achieves statistical signficance at the 0.05 level. They were developed when time on mainframe computers was at a premium and when statisticians were considering the problem of what to do when there were more predictors than observations. Forward selection starts with an empty model. The variable that would have the smallest P value if it was the only predictor in the regression is placed in the model. Each subsequent step adds the variable that has the smallest P value in the presence of the predictors already in the equation.
Backward elimination starts with all of the predictors in the model.
Each subsequent step removes the least significant variable in the model until all remaining variables have individual P values smaller than some value, such as 0.05 or 0.10, chosen at the start of the analysis. Stepwise regression is similar to forward selection except that variables are removed from the model if they become nonsignificant after other predictors are added. Backwards elimination has an advantage over forward selection and stepwise regression. It is possible for a set of variables to have considerable predictive capability even though any subset of them does not. Forward selection and stepwise regression will fail to locate this set.