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The mean is one of the most common measures of central tendency. In simple terms, it's what people usually refer to as the "average". Calculating the mean involves two steps. First, add up all the numbers in your data set. In this case, we have sales figures that add up to \\(77,000\\) over six months. The sales numbers are \\(11,000, 15,000, 11,000, 16,000, 12,000,\\) and \\(12,000\\). Second, divide the sum by the number of data points you have. Here, we have six data points, so the mean is \[\text{Mean} = \frac{77,000}{6} \approx 12,833.33\] This tells us that the average sales amount over six months is approximately \\(12,833.33\\). The mean can be advantageous when reporting higher performance as it considers all data points. However, it's crucial to keep in mind that the mean is susceptible to being skewed by very high or very low numbers, known as outliers.

The median is another measure of central tendency that is useful, especially when you have outliers in your data. While the mean takes all data points into consideration, the median focuses on the middle of the data set. Arranging data from smallest to largest helps us find the median. For this data set, arranged values are \(11,000, 11,000, 12,000, 12,000, 15,000, 16,000\). With an even number of data points, the median is the average of the two middle numbers. So here, we calculate: \[\text{Median} = \frac{12,000 + 12,000}{2} = 12,000\] Thus, the median sales figure is exactly \\(12,000\\). The median can be particularly useful as it is not influenced by extreme sales figures, providing a more stable measure of central tendency when outliers are present.

The mode helps identify the most frequently occurring value in a data set. Unlike the mean and median, a data set can have more than one mode, or even no mode at all if no number repeats. In our given sales figures, the numbers \(11,000\) and \(12,000\) appear twice each. Thus, this data set is bimodal, having two modes of \(11,000\) and \(12,000\). Identifying the mode can be helpful when trying to understand the most common outcome or frequency in your data, which can be valuable when setting targets or expectations based on past performance. It highlights values that recur most frequently, offering insight into typical results without being skewed by outliers or extreme values.