As a psychologist with a focus on research methodology, I can explain the concept of "p < 0.05" in psychology research. The term "p" stands for "probability," and in the context of statistical analysis, it refers to the likelihood of obtaining the observed results (or more extreme) if the null hypothesis were true. The null hypothesis is a statement of no effect or no difference, and it is what researchers attempt to disprove when they conduct a study.

When we say "p < 0.05," we are stating that there is less than a 5% probability that the observed results occurred by chance if the null hypothesis were true. In other words, if the null hypothesis were true, there would be less than a 5% chance that we would see the results we did. This is considered a threshold for statistical significance in many psychological studies.

The use of "p < 0.05" as a threshold is somewhat arbitrary but has historical roots. It was established as a convention by Ronald Fisher, a pioneer in the field of statistics, who suggested that a p-value of less than 0.05 could be used as a criterion for statistical significance. This threshold is used to balance the risk of making a Type I error (incorrectly rejecting a true null hypothesis) and a Type II error (failing to reject a false null hypothesis).

However, it's important to note that a "p < 0.05" does not mean that the results are definitive or that there is no chance of error. It simply means that the results are unlikely to be due to random variation alone. Researchers also consider the effect size, which is a measure of the magnitude of the difference or effect being studied. A small effect size with a "p < 0.05" might not be as meaningful as a large effect size with the same p-value.

Moreover, the reliance on "p < 0.05" has been criticized for promoting a culture of significance testing that can lead to questionable research practices, such as p-hacking, where researchers manipulate their data or analyses to achieve statistical significance.

In recent years, there has been a push towards using confidence intervals and Bayesian methods as alternatives to the traditional p-value approach. These methods provide a more nuanced understanding of the evidence and the uncertainty associated with research findings.

In summary, "p < 0.05" in psychology is a statistical measure that indicates whether the results of a study are likely to be due to chance or not. It is a widely used criterion for determining statistical significance, but it is not without its limitations and should be interpreted in the context of the study's design, the effect size, and the confidence intervals or evidence provided by alternative statistical methods.

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Statistical significance, often represented by the term p < .05, has a very straightforward meaning. If a finding is said to be --statistically significant,-- that simply means that the pattern of findings found in a study is likely to generalize to the broader population of interest. That.is.it.Sep 15, 2016read more >>