Data Interpretation Olympiad

Working out values when each square stands for more than one unit.

Tracking how a value changes over time.

Finding values at the intersection of a row and column.

Judging which of two charts shows a bigger or smaller value.

Reading how often something occurs from a basic table.

Interpreting familiar data like weather or sports scores.

Deciding which chart type best fits a given set of data.

Comparing two related values side by side on one chart.

Reading pictographs where one symbol represents several units.

Describing whether a value is rising, falling or steady.

Finding the average of a small, simple data set.

Judging how likely a simple event is to happen.

Turning a list of raw numbers into an organised table.

Identifying the largest and smallest slice of a pie chart.

Combining two or more values from a table to answer a question.

Working out the value each slice of a pie chart represents.

Finding all three measures for a small data set.

Organising data into a frequency table from a raw list.

Answering questions that need two readings from a line graph.

Reading and comparing three or more related charts.

Calculating the probability of a single simple event.

Understanding surveys, samples and how data gets gathered.

Reading a histogram and understanding equal class widths.

Organising larger data sets into equal class intervals.

Estimating the average using midpoints of class intervals.

Reading histograms with unequal class widths.

Building a running total from a frequency table.

Comparing predicted probability with actual trial results.

Interpreting graphs with more than one line or variable.

Converting chart values into percentages for comparison.

Spotting a shared trend across two different chart types.

Finding median and mode from grouped frequency tables.

Measuring how spread out a data set really is.

Plotting and reading a cumulative frequency curve.

Working out probability when two simple events combine.

Cross-referencing figures from two different data sources.

Identifying scales or axes designed to distort a chart's message.

Reading data expressed as ratios or proportions.

Translating a written scenario into a data question.

Combining mean, median and mode analysis on larger data sets.

Understanding what standard deviation tells you about spread.

Applying probability rules to real, messy data sets.

Reading a scatter plot to judge a relationship between variables.

Drawing one conclusion from several different data sources.

Going beyond the numbers to a reasoned conclusion.

Reading quartiles and outliers from a box plot.

Solving layered, multi-step data-based word problems.

Using all three measures to compare two data sets.

Using ogives to estimate the median of large data sets.

Applying probability to layered, real-world scenarios.

Judging the strength and direction of a relationship in data.

Using data evidence to justify a recommended decision.

Working with data sets that resemble genuine reports or news data.

Judging how reliable and unbiased a data source really is.

Applying everything learned to one extended data scenario.