Statistical testing
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Statistical testing is a crucial topic in data analysis and research. Here's what trusted experts have shared about it:

Importance of Effect Sizes in Big Data: With large data sets, conventional tests of statistical significance might become meaningless as even trivial results can appear significant. Instead, focus on effect sizes and practical implications for more meaningful analysis 1.

Understanding Statistical Inference: Statistical tests help determine if the observed differences in data are meaningful or due to chance. For instance, in comparing two groups, a t test or chisquare test can help establish if differences are statistically significant enough to reject the null hypothesis 2 3.

Choosing the Right Statistical Test: The choice of statistical test depends on the data type and research question. For instance, when dealing with ordinal variables, nonparametric tests like the WilcoxonMannWhitney test can be appropriate instead of ttests 4.

Limitations of Statistical Significance: Statistically significant results don't guarantee that an observed pattern isn't noise. Instead, they indicate the probability of observing such a pattern if there was no real effect. This distinction is crucial for accurate interpretation of research findings 5.
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