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          <given>Hanna</given>
        </name>
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      <item>
        <name>
          <family>Holmgren</family>
          <given>Mattias</given>
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          <given>Patrik</given>
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          <given>Emma</given>
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        <name>
          <family>Beaman</family>
          <given>C. Philip</given>
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          <given>Linden</given>
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    <title>Is the Negative Footprint Illusion Exacerbated by the Numerosity of 
Environment-Friendly Additions?</title>
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    <keywords>negative footprint illusion, averaging bias, numerosity, category-size effect</keywords>
    <abstract>Adding environment-friendly items (e.g., environmentally certified houses) to conventional items (e.g., conventional houses) leads people to believe that the carbon footprint of the entire set decreases – a negative footprint illusion. This illusion is supposedly underpinned by an averaging bias: people base environmental impact estimates not on the total impact of items but on their average impact. We demonstrate here that the illusion’s magnitude increases with the addition of a greater number of environment-friendly items, both when the number of conventional items is held constant (Experiment 1 and 4) and when the ratio between the conventional category and environment-friendly additions is held constant (Experiments 2 and 3), suggesting that item numerosity plays a role. However, Experiment 5 favoured an averaging bias account, independent of numerosity. The findings may be reconciled with the proposal that, under certain conditions, numerosity attenuates or intensifies the averaging bias, thereby modulating the negative footprint illusion’s magnitude. 

In this deposit we include raw data from Experiments 1 - 5, and an example &apos;scenario&apos; from an experiment.</abstract>
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    <publisher>Thinking &amp; Reasoning</publisher>
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        <family>Andersson</family>
        <given>Hanna</given>
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    <collection_method>Data were collected by means of web-based questionnaires created using Qualtrics and distributed to respondents residing in the UK via the Prolific Academic crowdsourcing platform. Please see the manuscript for details of each experimental design.</collection_method>
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