The first purpose of the project was to examine the internal consistency and structure of the English version of the Statistical Anxiety Rating Scale (STARS). Participants were 202 (79% females) psychology undergraduates at James Cook University. Participants completed the STARS, the Statistical Anxiety Scale, and the Attitudes toward Statistics scale. Acceptable internal consistency reliabilities, ranging from .81 to .94, were found in this sample. Approximate fit indices suggest that a correlated six first-order factor model best describes the data in contrast to theoretical considerations suggesting that a six factor model with two correlated superordinate factors (i.e. statistics anxiety and attitudes toward statistics) best describes the data. The second purpose of the project was to examine the role of attentional bias in statistics anxiety in three experiments. Participants were 94 (73% females), 99 (68% females), and 104 (67% females) psychology undergraduates at James Cook University, respectively. These participants had either never taken a statistics course before but expected to enrol in one in the future, were currently enrolled in a statistics course, or had successfully completed at least one statistics course but were not currently enrolled in a statistics course. Participants completed the emotional Stroop task and the dot probe task, the STARS, the Social Desirability Scale, and the State-Trait Anxiety Inventory. No statistically significant differences were found across the experiments, indicating an absence of attentional bias in statistics anxiety. Implications include a reconsideration of the cognitive mechanisms underlying statistics anxiety. Specifically, individuals with statistics anxiety might be interpreting danger based on the absence of safety indicators instead of the presence of danger indicators. Alternatively, another form of cognitive bias, such as an interpretation bias might underlie statistics anxiety. Future research should be conducted to compare the plausibility of these two explanations
