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A positive correlation indicates that as one variable increases the other variable tends to increase.
In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate.
The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.
The mean describes an entire sample with a single number that represents the center of the data. You calculate the mean by adding up all of the observations and then dividing the total by the number of observations.
For example, if the weights of five apples are 5, 5, 6, 7, and 8, the average apple weight is 6.4.5 7 6 5 9 / 5 = 6.4The mean is sensitive to skewed data and extreme values.
Random assignment uses chance to assign subjects to the control and treatment groups in an experiment.
This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study.The difficulty in definitively stating that a treatment caused the difference is due to potential confounding variables or confounders.Confounders are alternative explanations for differences between the experimental groups.Research Randomizer is a free resource for researchers and students in need of a quick way to generate random numbers or assign participants to experimental conditions.This site can be used for a variety of purposes, including psychology experiments, medical trials, and survey research.The extreme values of -1 and 1 indicate a perfectly linear relationship where a change in one variable is accompanied by a perfectly consistent change in the other.In practice, you won’t see either type of perfect relationship.The idea is to determine whether the effect, which is the difference between a treatment group and the control group, is statistically significant.If the effect is significant, group assignment correlates with different outcomes.For example, the mean difference between the health outcome for a treatment group and a control group is the effect. Consequently, samples are taken and a statistical test, such as a t-test or a one-way ANOVA, determines whether an effect exists and estimates its size.effect is statistically significant, but they cannot determine whether the treatment causes the effect. There’s a critical separation between significance and causality: In this post, learn how using random assignment in experiments can help you identify causal relationships.As a critical component of the scientific method, experiments typically set up contrasts between a control group and one or more treatment groups.