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Preface
## Excerpt from Chapter 3. Mistake: Not recognizing publication bias

By the time you read a paper, a great deal of selection has occurred. When experiments are successful, scientists tend to continue the project, while less successful projects get abandoned. When the project is done, scientists are more likely to write up projects that lead to remarkable results or keep analyzing the data in various ways to extract a statistically significant conclusion. Finally, journals are more likely to publish “positive” studies. If the null hypothesis were true, you would expect a statistically significant result in 5% of experiments.

But those 5% are more likely to get published than the other 95%. This is called publication bias. If many studies were performed, you might expect some studies to find larger effects, and some studies will to smaller effects, and for the average effect size to be close to the truth. However, studies with small effects tend not to get published. On average, therefore, the studies that do get published tend to report effect sizes that overestimate the true effect (Ioannidis, 2008).

## Excerpt from Chapter 20. Normality tests ask the wrong question

Because almost no variables you measure follow an ideal Gaussian distribution, why use tests that rely on the Gaussian assumption? Plenty of studies with simulated data have shown that the statistical tests based on the Gaussian distribution are useful when data are sampled from a population with a distribution that only approximates a Gaussian distribution. These tests are fairly robust to violations of the Gaussian assumption, especially if the sample sizes are large and equal.

When analyzing data, the question that matters is not whether the data were sampled from an ideal Gaussian population but whether the distribution from which they were sampled is close enough to the Gaussian ideal that the results of the statistical tests are still useful. Normality tests do not answer this question.

## Excerpt from Chapter 26 (Review)

#### **Statistical inference
helps you make general conclusions from limited data, ****so conclusions are always
presented in terms of probability**

Be wary if you ever
encounter statistical conclusions that seem 100% definitive.

** All statistical
tests are based on assumptions**

Review the list of
assumptions before interpreting any statistical results.

** Decisions about how
to analyze data should have been made in advance**

Otherwise the
investigators may be P-hacking.

** Statistics is only
part of interpreting data**

Also think about
study design and experimental methods.

** Many statistical
terms are also ordinary words**

Don’t mistakenly
give a statistical term an ordinary meaning.

**The standard error of the
mean does not quantify variability**

The standard
deviation and standard error of the mean are often confused.

**Confidence intervals
quantify precision**

All values (means,
difference, ratio, etc.) computed from data should be reported with a
confidence interval.

**Every P value tests a
null hypothesis**

You cannot understand a P
value until you can precisely state the corresponding null hypothesis.

**The concept of statistical
significance is designed to help you make a decision based on one result**

If you don’t plan
to use this one result to make a crisp decision, the concept
of statistical significance is not necessary.

**“Statistically
significant” does not mean the effect is large or scientifically important**

It only means a
difference (or association, or correlation) this large as or larger will happen
less than 5% of the time (or some other stated value) by chance alone.

**“Not significantly
different” does not mean the effect is absent, small, or scientifically
irrelevant**

All you can
conclude that the observed results are not inconsistent with the null
hypothesis.

**The term significant has
two meanings, so is often misunderstood**

Avoid the term when
possible.

**Multiple comparisons make
it hard to interpret statistical results**

To correctly
interpret statistical analyses, all analyses must be planned before collecting
data, and all planned analyses must be conducted and reported.