Abstract
The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.
Highlights
In the field of behavioural neurosciences, as well as in every scientific discipline, sample size definition is a crucial factor for ensuring the reliability and accuracy of the results obtained in a study, as well as the replicability of the study itself [1]
The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration
The inclusion of an adequate number of participants in neuroscientific studies is an important issue, and some authors reported alarming data about the potential risks of employing a small sample size [1]. This is the case of Button and colleagues [2], who claimed that many of the works published in neuroscientific domain are not reproducible due to their small sample size and the low statistical power associated with false-positive effects counted in the results
Summary
In the field of behavioural neurosciences, as well as in every scientific discipline, sample size definition is a crucial factor for ensuring the reliability and accuracy of the results obtained in a study, as well as the replicability of the study itself [1]. In order to prevent and mitigate consequences of a poor statistical power, it is generally recommended to estimate the proper sample size To this end, different studies recently tried to propose methods to calculate the required sample size while designing the experimental protocol [3,4,5,6,7,8]. The inclusion of an adequate number of participants in neuroscientific studies is an important issue, and some authors reported alarming data about the potential risks of employing a small sample size [1] This is the case of Button and colleagues [2], who claimed that many of the works published in neuroscientific domain are not reproducible due to their small sample size and the low statistical power associated with false-positive effects counted in the results. A smaller sample size, in these cases, is chosen to undermine the statistical power of the study, enhancing the presence of false-positive effects which endorse the desired results
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