In our example, when the unspecified “data” is likened to a “flash in the pan”, we may safely infer that those figures will not remain the same the next time a new set of figures is released.
For example, many factories in China typically don’t produce a lot during the first quarter of the year. That’s due to the Spring Festival, which usually falls on January or February. Workers, especially those from the countryside, take between one week and a full month off during this period – spending the time with their family.
Therefore, if production figures are not very good, bosses at these factories usually don’t fret a lot, knowing that they will pick up when all workers are back to their post and all machines are up and running, firing on all cylinders, so to speak.
On the other hand, for another example, high street retail sales in America are typically robust in December. That’s thanks to the holiday season of Christmas. Some shops make half of their annual profits during Christmas sales alone. So therefore, if the December sales figures are good, don’t be overjoyed because those figures cannot be repeated any other month of the year.
So, please “don’t overreact to data”, or anything, “that may prove to be a flash in a pan.”
Now, a few more media examples of the idiom “flash in the pan”, meaning anything that dazzles and catches our attention but soon disappears, failing to make a lasting impression:
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