Although most people are not aware of it, bias can occur when interpreting graphs. Within-the-bar bias describes a misinterpretation of the distribution of data underlying bar graphs that indicate an average or where the average estimation point moves inside the bar when the average of several graphs is estimated. This study proposes and tests two methods based on information processing to reduce within-the-bar bias. The first method facilitates bottom-up processing by changing various graph features, such as presenting confidence intervals, placing boundaries around the graph, and showing cumulative bars with different tones. The second method facilitates top-down processing by instructing participants to estimate the mean based on a dot at the end of each bar. Testing of the first method showed that cumulative bars reduced bias, but the other methods did not. The second method was found to reduce bias. Overall, our results demonstrate that the accurate interpretation of bar graphs can be facilitated through the manipulation of specific graph features and instruction.

尽管大多数人对此并不了解，但是在解释图形时可能会出现偏差。条形内偏差描述了对条形图基础数据的分布的误解，这些条形图指示平均值，或在估计多个图的平均值时，平均估计点在条形内的移动位置。这项研究提出并测试了两种基于信息处理的方法，以减少门槛偏差。第一种方法通过更改各种图形特征（例如呈现置信区间，在图形周围放置边界以及显示具有不同色调的累积条）来促进自下而上的处理。第二种方法通过指示参与者基于每个小节结尾处的点来估计均值来促进自上而下的处理。第一种方法的测试表明，累积的条形减少了偏差，但是其他方法没有。发现第二种方法可以减少偏差。总体而言，我们的结果表明，可以通过操纵特定的图形功能和指令来方便条形图的准确解释。