What is Data Processing?
In science, collecting data is only the first step. Once you've gathered your measurements, you need to process that data — organise it, calculate meaningful values, and look for patterns. This is what transforms raw numbers into useful information.
The organisation, calculation, and analysis of raw data to extract meaningful information and identify patterns or trends.
Think about it this way: if you measured the heights of 30 plants in an experiment, a list of 30 numbers doesn't tell you much at a glance. But if you calculate the average height, find the range, and organise the data into a table or graph, suddenly you can see what's happening.
Data processing in MYP Sciences typically involves:
- Organising raw data into tables
- Calculating measures of central tendency (mean, median, mode)
- Calculating measures of dispersion (range, interquartile range, standard deviation)
- Presenting data in appropriate graphs and charts
- Identifying patterns, trends, and anomalies
Quick Review: Measures of Central Tendency
Before we dive into dispersion, let's make sure we're confident with the three main measures of central tendency — these tell us about the centre or typical value of a dataset.
The sum of all values divided by the number of values. Often called the "average."
The middle value when all data points are arranged in order from smallest to largest. If there is an even number of values, the median is the mean of the two middle values.
The value that appears most frequently in a dataset. A dataset can have no mode, one mode, or multiple modes.
A student measures the time (in seconds) for a ball to roll down a ramp in 7 trials:
Data: 2.3, 2.5, 2.4, 2.3, 2.6, 2.4, 2.3
Mean: s
Median: Arrange in order: 2.3, 2.3, 2.3, 2.4, 2.4, 2.5, 2.6 → Median = 2.4 s
Mode: 2.3 s (appears 3 times)
The mean is sensitive to outliers (extreme values), while the median is more robust. In science, we often report the mean, but always check whether any anomalous results are pulling it away from the true centre.
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