As scientists, we have all received some level of training in statistics. A fundamental concept is that we are trying to make inferences about a specific population, but that we only have access to a sample of the people, dogs, amoebas, etc that belong to that population. By randomly sampling amoebas for example, we collect data and conduct statistical tests
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Spike2 is a piece of software written by the folks at CED (Cambridge Electronic Design) to collect data with their data acquisition boards. As pointed out in a previous post, Spike2 can be used to analyse data. However, the drop-down menus and options make it difficult to ensure processing is consistent from session to session. Spike2 comes with its own
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In a previous post, we saw that the number of subjects or samples in our study does not influence the rate of false-positive findings. In this post we will learn how sample size influences estimates of the size of studied effects. If the true effect of a medication is to reduce heart rate by 10 beats per minute, how well
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Competition in science is ever increasing. Research funds are harder to come by and positions have increasing number of applicants. Being the first to publish a result has a disproportionately large impact on prestige and advancement. These problems were already present a decade ago when Anderson et al. (2007) published their paper entitled The perverse effects of competition on scientists’
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As scientists we collect data and look for patterns or differences. Because populations display variation and we are unable to collect data from all members of a population, statistical results will always possess a level of uncertainty. For example, it is common to set alpha to 0.05. This implies that if there is no difference or effect, there is a
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In our previous post, we revisited the Ioannidis argument on Why most published research findings are false. Other factors such as p-hacking can also increase the chance of reporting a false-positive result. Such results are associated with a p-value deemed to be statistically significant, but the underlying hypothesis is in fact false. Researcher degrees of freedom As scientists, we have
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