1. Spot Misleading Averages
4 min
One idea
An average is a summary, not the full story. Before you claim a number proves something, check whether a few extreme values are pulling it up or down.
Why this matters
Hackathon projects often show numbers like average wait time, average spend, average score, or average study hours. These are useful, but they can mislead recruiters, judges, and users if the data is uneven.
Your job is not to avoid averages. Your job is to use them with judgement.
Mean vs median
The mean is what most people call the average: add all values, then divide by count. The median is the middle value after sorting.
If the mean and median are close, the average is usually okay as a quick summary. If they are far apart, say why before making a claim.
UPI wait example
Say your project tracks payment confirmation time for 9 UPI transactions in a campus canteen. One transaction was delayed because the network dropped.
The mean says users waited about 18 seconds. The median says a typical user waited 7 seconds. Both are true, but they tell different stories.
times = [5, 6, 6, 7, 7, 8, 9, 10, 105]
mean = sum(times) / len(times)
sorted_times = sorted(times)
median = sorted_times[len(times) // 2]
print("mean:", round(mean, 1))
print("median:", median)
# Output:
# mean: 18.1
# median: 7