The paper examines relationships between the conditional Shannon entropy and the expectation of $\ell_α$-norm for joint probability distributions. More precisely, we investigate the tight bounds of the expectation of $\ell_α$-norm with a fixed conditional Shannon entropy, and vice versa. As applications of the results, we derive the tight bounds between the conditional Shannon entropy and several information measures which are determined by the expectation of $\ell_α$-norm, e.g., the conditional Rényi entropy and the conditional $R$-norm information. Moreover, we apply these results to discrete memoryless channels under a uniform input distribution. Then, we show the tight bounds of Gallager's $E_{0}$ functions with a fixed mutual information under a uniform input distribution.