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Calculating percentages is a handy tool in figuring out proportions of a whole in fractions of 100. Using the given problem as a reference, to calculate the percentage of voters who chose Kennedy in the 1960 presidential election, start by finding the total number of votes cast. This is the sum of votes for all candidates including Kennedy, Nixon, and third-party candidates. In this scenario, you sum up their votes:

• Kennedy: 34,226,731 votes
• Nixon: 34,108,157 votes
• Third-party candidates: 197,029 votes

This totals to 68,531,917 votes.
Next, apply the formula for percentage calculation: \[\text{Percentage} = \left(\frac{\text{Number of Kennedy voters}}{\text{Total number of voters}}\right) \times 100 \]Substituting the numbers in our example, you get: \[\left( \frac{34,226,731}{68,531,917} \right) \times 100 \]Solving this gives you Kennedy's vote share in percentage. Understanding how to compute percentages allows you to convey data in an easily understandable form.

Confidence intervals are useful in statistics for estimating the reliability of a sample statistic. It offers a range where the true population parameter is likely to be found. Typically, confidence intervals are constructed around population parameters when working with sampled data to estimate characteristics of the total population.
In the case of this presidential election, we are not using a sample; we have complete voter data for Kennedy, Nixon, and the third-party candidates. Because of this, we know the exact proportion, leaving zero room for estimate error or variability.
Without sampling, there's no need to construct a confidence interval since it adds no new information. The concept becomes unnecessary when dealing with entire populations as opposed to samples. So, in this case, a confidence interval is not appropriate.

Data interpretation involves examining numbers or data to draw meaningful insights or conclusions. In our example of the 1960 presidential election, determining the percentage of voters that favored Kennedy gives us a clear numerical insight into his relative popularity.
In general, interpreting such data requires understanding the context—like recognizing the total votes cast are an absolute figure indicating actual popularity, rather than an estimate or prediction. It also allows you to draw comparisons with other candidates or prior elections.

• Kennedy's percentage directly compares his support to other candidates.
• Real-world implications of the data offer insights for various analyses such as voter turnout and candidate popularity.

Data interpretation is critical for creating messages from numbers, but it also depends on the precision and completeness of data collected. Here, the full dataset eliminates uncertainties present in models based solely on sampling. Thus, it ensures more accurate conclusions drawn from the data.