The significance of a skip pattern depends on the question leading to the skip pattern, the questions within that skip pattern, and the variables you intend to analyze. If you fail to check for skip patterns, you may obtain only a proportion of the population, instead of the entire study population.
For example, from the sample person questionnaire in the Locate Variables module, you should note that some of the components, such as the blood pressure questionnaire, have skip patterns. In the blood pressure questionnaire, respondents were not asked any other questions relevant to high blood pressure (hence, the questions were skipped over) if they said " No" to question hae2: " Have you ever been told by a doctor or other health professional that you had hypertension?" Those who answered " Yes" to this question were asked additional questions related to blood pressure, such as hae3, " Were you told on 2 or more different visits that you had hypertension, also called high blood pressure?"
If you would like to estimate the prevalence of diagnosed hypertension (defined as at least two occurrences of a person ever being told by a doctor that he or she had hypertension) among US adults, you must recode hae3 to include those who answered " No" in hae2. Thus, until you recode hae3 (or define a new variable based on it) to include those who answered " No" to hae2, these people will be left out of the denominator value. If you fail to do this step, you will obtain the proportion of diagnosed hypertension only among a subpopulation of people who have ever been told by a doctor that they had hypertension, instead of the entire study population.