caption: Random allocation IQ and number of hours studied will not influence the mean exam score in the blueberry group or the sugar cube group.
exif: {"Image DocumentName": "Random allocation IQ and number of hours studied will not influence the mean exam score in the blueberry group or the sugar cube group.", "Image ImageDescription": "We will be able to plot two frequency histograms of the exam scores, one for each group. and calculate the SD. Can IQ scores and hours spent studying influence the SD of the scores in the two groups? imagine we run the experiment once using a sample containing great variation in IQ and study hours. imagine we run the experiment again using a sample selected so that the IQ\u2019s only vary between 100 and 110, and everyone has similar studying habits. What implication could this have for the ability of the experiment to produce a statistically significant result? IQ and hours studied will both contribute to the variation in exam scores in both experimental groups. This means that they both have the effect of increasing the SD (which will increase what Andy Field calls unsystematic variation). Because the SE depends on sample size, but also depends on the SD, it means the SE will be bigger too. With a bigger SE it is harder to find a statistically significant difference between conditions. You can imagine that the confidence intervals on a graph are more likely to overlap between the two conditions if the SE is bigger. So, in the experiment where we select people to have a narrow range of IQ and similar studying habits, and then randomly allocate them to the blueberry and sugar cube conditions you are going to get smaller SD of exam scores in both experimental groups. This means you can find a statistically significant effect of blueberries using less participants. Sometimes it helps to try and minimise the unsystematic variation in the experiment. Randomization prevents confounding variables from influencing the means, but they still contribute to variation in scores, which we have to designate as measurement error in our statistics. Big measurement error is bad news because it makes it hard to find statistically significant differences.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}