Shark Attacks Explain how the following graph is misleading.

Verified step by step guidance

1. Step 1: Observe the y-axis scale of the bar graph. Notice that it starts at 0 and goes up to 150, but the increments are not consistent or clearly marked, which can distort the visual comparison between months.
2. Step 2: Examine the height of the bars relative to the y-axis. The bars for months with fewer attacks (like January and December) appear very small compared to months with many attacks (like September), but the visual difference might be exaggerated due to the scale and spacing.
3. Step 3: Consider the time frame represented by the data (1926 to present). The graph aggregates data over many years, which may hide trends or changes in shark attacks over shorter periods, potentially misleading the viewer about current risk levels.
4. Step 4: Look at the spacing and width of the bars. If the bars are not uniformly spaced or sized, it can create a misleading impression of the data distribution across months.
5. Step 5: Reflect on the context of the data. The graph shows raw counts of attacks but does not account for factors like the number of people in the water each month, which affects the risk and could lead to misinterpretation of the danger in different months.

Key Concepts

Scale Manipulation

The vertical axis scale can be manipulated to exaggerate or minimize differences between data points. In this graph, the y-axis starts at zero, which is good practice, but the spacing and increments can still influence perception of the differences in shark attacks across months.

Context and Denominator Effect

The graph shows raw counts of shark attacks without considering the number of people in the water each month. Higher attacks in summer months may be due to more swimmers, so without adjusting for exposure, the graph can mislead about actual risk.

Data Aggregation Over Time

The graph aggregates data from 1926 to present, which may mask trends or changes over time. Seasonal patterns might have shifted, and combining all years can give a misleading impression of current risk patterns.