Final answer:
Single exponential smoothing places greater emphasis on the most recent data, reacting strongly to recent changes in a stable time series. It does not prioritize old data, nor is it swayed by randomness or disregard history.
Step-by-step explanation:
In the context of a fairly stable time series with relatively little random variability, the correct statement about single exponential smoothing (SES) is that SES reacts strongly to recent changes in data. This method is designed to forecast future values by applying smoothing constants to the most recent data points, thus giving them more weight than older observations. This means that option a) is correct. SES does not place more weight on distant past observations, nor is it heavily influenced by random fluctuations, and it certainly does not ignore historical data completely. In comparison to SES, a method that emphasizes older data would be double exponential smoothing or Holt's linear trend method.
Regarding the contextual information, political scientists may be able to make better predictions with lots of data and advanced analysis techniques, but the predictability of events also play a vital role. For example, random events are inherently unpredictable, making the predictive capability of even the best scientists and technology limited in scenarios of high randomness.