Data Needs Context
4 September 2023
Data needs context. Data alone cannot move someone to act or shift their perspective. In fact, when people see data without context, they are likely to see that data as a manifestation of what they already believe. Numbers mean nothing in and of themselves. We assign them meaning.
Data and story can work hand in hand. Imagine a forest before you: if that forest is the data that you have available and each tree is a different data point, imagine you and others entering that forest at different points in time. Left to your own devices, and without any sort of signals, you and others who were traversing that forest would more than likely exit at different points. The same is true with data. Think of a compelling story as a dirt path that guides everyone through the forest to come out at the same point.
Using raw data can be counterproductive in two ways.
1. Large numbers trigger pseudo-inefficacy.
While large numbers may shock or horrify us, they also have the strange effect of making us believe that we ourselves can’t make a difference. Mother Theresa famously said, “if I look at the mass, I will never act. If I look at the one, I will.”
Research backs her up. Research conducted by renowned scholar Paul Slovic suggests that when we are asked to help more than even just one person, we are less likely to make moves to help. So, larger and larger numbers diminish our personal sense of agency.
2. Without context, data affirms our existing beliefs.
Work by scholar Rebecca Hetey shows that using statistics to inform the public about racial injustice can backfire, leading them to support policies that exacerbate rather than ameliorate the problem. In one of her studies, she and a fellow scholar found that white Americans did not support criminal justice reform when they read statistics about how Black people are over-represented in prison. Nearly 40 percent of the prison population is African-American, but blacks are 13 percent of the US population. After they heard this data, study participants were more supportive of policies like California’s Three Strikes law and New York City’s stop-and-frisk policy that profile people by race and lead to higher rates of incarceration–exactly the opposite shift that advocates hoped for.
Sharing data about the prevalence of forced and child labour is crucial for documenting the challenge and showing trends. It is important, however, to connect that data to what’s working to reduce the numbers in similar regions or sectors. Where you can, draw on evaluations of other efforts that show the connection between specific changes and changes in the numbers of people experiencing forced and child labour.
Data and story can work hand in hand. Imagine a forest before you: if that forest is the data that you have available and each tree is a different data point, imagine you and others entering that forest at different points in time. Left to your own devices, and without any sort of signals, you and others who were traversing that forest would more than likely exit at different points. The same is true with data. Think of a compelling story as a dirt path that guides everyone through the forest to come out at the same point.
Using raw data can be counterproductive in two ways.
1. Large numbers trigger pseudo-inefficacy.
While large numbers may shock or horrify us, they also have the strange effect of making us believe that we ourselves can’t make a difference. Mother Theresa famously said, “if I look at the mass, I will never act. If I look at the one, I will.”
Research backs her up. Research conducted by renowned scholar Paul Slovic suggests that when we are asked to help more than even just one person, we are less likely to make moves to help. So, larger and larger numbers diminish our personal sense of agency.
2. Without context, data affirms our existing beliefs.
Work by scholar Rebecca Hetey shows that using statistics to inform the public about racial injustice can backfire, leading them to support policies that exacerbate rather than ameliorate the problem. In one of her studies, she and a fellow scholar found that white Americans did not support criminal justice reform when they read statistics about how Black people are over-represented in prison. Nearly 40 percent of the prison population is African-American, but blacks are 13 percent of the US population. After they heard this data, study participants were more supportive of policies like California’s Three Strikes law and New York City’s stop-and-frisk policy that profile people by race and lead to higher rates of incarceration–exactly the opposite shift that advocates hoped for.
Sharing data about the prevalence of forced and child labour is crucial for documenting the challenge and showing trends. It is important, however, to connect that data to what’s working to reduce the numbers in similar regions or sectors. Where you can, draw on evaluations of other efforts that show the connection between specific changes and changes in the numbers of people experiencing forced and child labour.
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