The independent and paired T-test in jamovi

This is a short tutorial on comparing two means with the independent and paired t-test in jamovi.

Description of the data

I have downloaded the dataset from the Mathematics and Statistics Help (MASH) site. The dataset contains data on 78 persons following one of three diets. I will use the dataset to show you how to estimate the difference between two means with the independent t-test analysis and the dependent t-test analysis in jamovi. I will ignore the different diets and focus on gender differences and pre- and post-weight differences instead.

I will focus on two substantive research questions.

  1. To what extent does following a diet lead to weight loss?
  2. To what extent does the weight lost differ between females and males?

The paired t-test in jamovi

Let’s start with the first research question. Statistical analysis can be useful for answering the research question, but then we will first have to translate the substantive question into a statistical one. Now, it seems reasonable to assume that if following a diet leads to weight loss, that the typical weight before the diet differs from the typical weight after following the diet. If we assume furthermore that the mean provides a good representation of what is typical, then it is plausible that the mean weight before the diet differs from the mean weight after the diet.

If we are interested in the extent to which means differ, we are – statistically speaking – interested in the extent to which expected values differ. So, our analysis will focus on finding out what our data have to say about the difference between the expected values. More concrete: we focus on the difference between the expected values for weight (measured in kg) before and after the diet. Using conventional symbols, we aim at uncovering quantitative information about the difference \mu_{pre\ weight} – \mu_{post\ weight}.

Since all persons were measured pre and post diet, the measurements are likely to be correlated. Indeed, the sample correlation equals r = .96. We need to take this correlation into account and that is why we use the statistical techniques for estimation and testing that are available in the paired t-test analysis in jamovi.

Doing the paired t-test in jamovi

In a real research situation, we would of course start with descriptive analyses to figure out what the data seem to suggest about the extent to which a diet leads to weight loss. But now we are just looking at how to obtain the relevant inferential information from jamovi.

I have chosen the following options for the analysis.

Choosing Jamovi paired t-test options
Figure 1. Paired t-test analysis options in jamovi

Since we are interested in the extent to which following a diet leads to weight loss, it is important to realize that the t-test in itself does not necessarily provide useful information. Why? Because the t-test gives us input to make the decision whether to reject the null-hypothesis that the expected values are equal, and only indirectly provides us with the information about the extent to which the expected values differ, and the latter is of course what we are interested in: we want quantitative information! The more useful information is provided by the estimate of the mean difference and the 95% Confidence Interval.

The paired t-test output

The relevant output for the t-test and the estimation results are presented in Figure 2.

Jamovi paired t-test output
Figure 2. Significance test and estimation results of the paired t-test analysis in jamovi

Let’s start with the t-test. The conventional null-hypothesis is that the expected values of the two variables are equal ( \mu_{pre\ weight} = \mu_{post\ weight}, or \mu_{pre\ weight} – \mu_{post\ weight} = 0. . The alternative hypothesis is that the expected values are not equal. Following convention, we use a significance level of \alpha = .05, so that our decision rule is to reject the null-hypothesis if the p-value is smaller than .05 and to not reject but also not accept the null-hypothesis if the p-value is .05 or larger.

The result of the t-test is t(77) = 13.3, p < .001. Since the p-value is smaller than .05 we reject the null-hypothesis and we decide that the expected values are not equal. In other words, we decide that the population means are not equal. As we said above, this does not answer our research question, so we’d better move on to the estimation results.

The estimated difference between the expected values equals 3.84 kg, 95% CI [3.27, 4.42]. So, we estimate that the difference in expected weights after 10 weeks of following the diet is somewhere between 3.72 and 4.42 kilograms.

Cohen’s d for the paired design

Jamovi also provides point and interval estimates for Cohen’s d. This version of Cohen’s d is derived by standardizing the mean difference using the standard deviation of the difference scores. Figure 3 presents the relevant output. The estimated value of Cohen’s delta equals 1.51, 95% CI [1.18, 1.83]. Using rules of thumb for the interpretation of Cohen’s d, these results suggest that there may be a very large difference in mean weights of the pre- and postdiet measurements.

Figure 3. Estimation results from the paired t-test analysis in jamovi

There is an alternative conceptualisation of the standardized mean difference. Instead of using the SD of the difference scores, we may use the average of the SDs of the two measurements. See for an explanation and R-code for the calculation of the CI.

The independent t-test in jamovi

To answer the second research question, we will have to reframe the substantive question into a statistical one. Just like we assumed above, we will consider the difference between the expected values (or population means) to be the statistical quantity of interest.

The conventional statistical null-hypothesis of the t-test is that the expected value of the variable does not differ between the groups or conditions. In other words, the null-hypothesis is that the two population means are equal. If the test result is significant, we will reject the null-hypothesis and decide that the population means are not equal. Note that this does not really answer the research question. Indeed, we are interested in the extent to which the expected values differ and not in whether we can decide that the difference is not zero. For that reason, estimation results are usually more informative than the results of a significance test.

Doing the independent t-test in jamovi.

I have chosen the following options for the independent t-test analysis in jamovi.

Options for the independent t-test in jamovi.
Figure 4. Options for the independen t-test in jamovi

The above options will give you the results of the independent t-test and, more importantly, the estimation results, both unstandardized and standardized (Cohen’s d).

The independent t-test output

The relevant output is presented in Figure 5.

Output of the independent t-test analysis in jamovi. Including significance test and estimation results.
Figure 5. Output of the independent t-test analysis in jamovi

The result of the t-test is t(74) = -0.21, p = 0.84. This test result is clearly not significant, so we cannot decide that the population means differ. Importantly, we can also not decide that the population means are equal. That would be an instance of accepting the null-hypothesis and that is not allowed in NHST.

The estimation results (i.e. -0.12 kg, 95% CI [-1.29, 1.04]) make it clear that we should not necessarly believe that the population means are equal. Indeed, even though the estimated difference is only 0.12 kilograms, the CI shows the data to be consistent with differences up to 1 kg in either direction, i.e. with women showing more average weight loss than men or women showing less average weight loss than men.

Cohen’s d for the independent design

We can also find the standardized mean difference and its CI in the independent t-test output: Cohen’s d = -0.08, 95% CI [-0.50, 0.41]. In this case, Cohen’s d is based on the pooled standard deviaton. According to rules-of-thumb that are used frequently in psychology the estimated effect is negligible to small, but the CI shows the data to be consistent with medium effect sizes in either direction.

The Anatidae Principle

If it looks like a duck, and quacks like a duck, we have at least to consider the possibility that we have a small aquatic bird of the family Anatidae on our hands.
– Douglas Adams

I like to teach my students how they can apply to their data-analysis what I call the Anatidae Principle (or the Principle of the Duck). (The name is obviously inspired by the above quote from Douglas Adam’s Dirk Gently’s Holistic Detective Agency).

For the purpose of data-analysis, the Anatidae Principle simply boils down to the following: If it looks like you found a relation, difference, or effect in your sample you should at least consider the possibility that there indeed is a relation, difference or effect. That is, look at your data, summarize, make figures, and think (hard) about what your data potentially mean for the answer to your research question, hypotheses, hunches, whatever you like. Do this before you start calculating p-values, confidence intervals, Bayes Factors, Posterior distributions, etc., etc.

In my experience, researchers too often violate the Anatidae Principle: they calculate a p-value, and if it is not significant they simply ignore their sample results. Never mind that, as they predicted, group A  outperforms group B, if it is not significant, they will claim they found no effect. And, worse still, believe it.

Kline (2013) ) (p. 117) gives solid advice:

“Null hypothesis rejections do not imply substantive significance, so researchers need other frames of reference to explain to their audiences why the results are interesting or important. A start is to learn to describe your results without mention of statistical significance at all. In its place, refer to descriptive statistics and effect sizes and explain why those effect sizes matter in a particular context. Doing so may seem odd at first, but you should understand that statistical tests are not generally necessary to detect meaningful or noteworthy effects, which should be obvious to visual inspection of relatively simple kinds of graphical displays (Cohen, 1994). The description of results at a level closer to the data may also help researchers to develop better communication skills.”

What is NHST, anyway?

I am not a fan of NHST (Null Hypothesis Significance Testing). Or maybe I should say, I am no longer a fan. I used to believe that rejecting null-hypotheses of zero differences based on the  p-value was the proper way of gathering evidence for my substantive hypotheses. And the evidential nature of the p-value seemed so obvious to me, that I frequently got angry when encountering what I believed were incorrect p-values, reasoning that if the p-value is incorrect, so must be the evidence in support of the substantive hypothesis. 
For this reason, I refused to use the significance tests that were most frequently used in my field, i.e. performing a by-subjects analysis and a by-item analysis and concluding the existence of an effect if both are significant,  because the by-subjects analyses in particular regularly leads to p-values that are too low, which leads to believing you have evidence while you really don’t.  And so I spent a huge amount of time, coming from almost no statistical background – I followed no more than a few introductory statistics courses – , mastering mixed model ANOVA and hierarchical linear modelling (up to a reasonable degree; i.e. being able to get p-values for several experimental designs).  Because these techniques, so I believed, gave me correct p-values. At the moment, this all seems rather silly to me. 
I still have some NHST unlearning to do. For example, I frequently catch myself looking at a 95% confidence interval to see whether zero is inside or outside the interval, and actually feeling happy when zero lies outside it (this happens when the result is statistically significant). Apparently, traces of NHST are strongly embedded in my thinking. I still have to tell myself not to be silly, so to say. 
One reason for writing this blog is to sharpen my thinking about NHST and trying to figure out new and comprehensible ways of explaining to students and researchers why they should be vary careful in considering NHST as the sine qua non of research. Of course,  if you really want to make your reasoning clear, one of the first things you should do is define the concepts you’re reasoning about. The purpose of this post is therefore to make clear what my “definition” of NHST is. 
My view of NHST  is very much based on how Gigerenzer et al. (1989) describe it: 
“Fisher’s theory of significance testing, which was historically first, was merged with concepts from the Neyman-Pearson theory and taught as “statistics” per se. We call this compromise the “hybrid theory” of statistical inference, and it goes without saying the neither Fisher nor Neyman and Pearson would have looked with favor on this offspring of their forced marriage.” (p. 123, italics in original). 
Actually, Fisher’s significance testing and Neyman-Pearson’s hypothesis testing are fundamentally incompatible (I will come back to this later), but almost no texts explaining statistics to psychologists “presented Neyman and Pearson’s theory as an alternative to Fisher’s, still less as a competing theory. The great mass of texts tried to fuse the controversial ideas into some hybrid statistical theory, as described in section 3.4. Of course, this meant doing the impossible.” (p. 219, italics in original). 
So, NHST is an impossible, as in logically incoherent, “statistical theory”, because it (con)fuses concepts from incompatible statistical theories. If this is true, which I think it is, doing science with a small s, which involves logical thinking, disqualifies NHST as a main means of statistical inference. But let me write a little bit more about Fisher’s ideas and those of Neyman and Pearson, to explain the illogic of NHST. 

I will try to describe the main characteristics of  the two approaches that got hybridized in NHST at a conceptual level. I will have to simplify a lot and I hope these simplifications do little harm. Let’s start with Fisher’s significance testing. 

Fisher’s significance testing

The main purpose of Fisher’s significance testing is gathering evidence about parameters in a statistical model on the basis of a sample of data. So, the nature of the approach is evidential. Crucially, the evidence the data provides can only be evidence against a statistical model, but it can not be evidence in favour of the model, much in line with Popper’s idea  of progress in science by means of falsification. The statistical model to be nullified, i.e. the model one tries to obtain evidence against, is called the null-hypothesis.

Conceptually, the statistical model is a descriptive model of a population of possible values. An important part of Fisher’s approach is therefore to judge what kind of model provides an appropriate model of the population. For instance, this process of formulating the model (which, of course, involves a lot of thought and judgement) may lead one to assume that the random variable has a normal distribution, which is characterized by only two parameters, μ the expected value or mean of the distribution and σ, the square root of the variance of the distribution, which in the case of the normal distribution is it’s standard deviation (the standard deviation is the square root of the variance).

The values of μ and σ (or σ2) are generally unknown, but we may assume (again as a result of thinking and judging) that they have particular values. For reasons of exposition, I will now assume that the value of σ is known, say σ = 15, so that we only have to take the unknown value of μ into account. Let’s suppose that our thinking and judging has led us to assume that the unknown value of μ = 100.  The null-hypothesis is therefore that the variable has a normal distribution with μ = 100, and σ = 15.

We can obtain evidence against this null-hypothesis, by determining a p-value. We first gather data, say we take a random sample of N = 225 participants, which enables us to obtain observed values of the variable. Next, we calculate a test statistic, for example by estimating the value of  μ (on the basis of our data) subtracting the hypothesized value and dividing the estimate by it’s standard error. Our estimated value may for example be 103, and the standard error equals 15 / √225 = 1.0, so the value of the test statistic equals (103 – 100) / 1 = 3. And now we are ready to calculate the p-value.

The p-value is the probability of obtaining (when sampling repeatedly) a value of the test statistic as large as or larger than the one obtained in the study, provided that the null-hypothesis is true. This probability can be calculated because the exact distribution of the test statistic can be deduced from the specification of the null-hypothesis. In our example, the test statistic is approximately normally distributed with μ = 0, and σ = 1.0. (The distribution is approximately normal, assuming the null-hypothesis is true, so the p-value in our example not exact). The p-value equals 0.003. (This is the so-called two-sided p-value, it is the probability of obtaining a value equal or larger than 3 or equal of smaller than -3, but we will ignore the technicalities of two-sided tests).

The p-value tells us that if the null-hypothesis is true, and we repeatedly take random samples from the population (as described by the null-hypothesis) we will find a value of our test statistic or a larger value in 0.3% of these samples. Thus, the probability of obtaining a value equal to or larger than 3.0 is very small.

Following Fisher, this low p-value can be interpreted as that something “improbable” occurred (assuming the null-hypothesis is true) or as inductive evidence against the null-hypothesis, i.e. the null-hypothesis is not true. 

In his early writings Fisher proposed a p-value smaller than .05 as inductive evidence against the null-hypothesis (keeping in mind the possibility that the null is true, but that something improbable happened), but later he thought using the fixed criterion of .05 to be non-scientific.  If the p-value is smaller than the criterion (say .05), the result is statistically significant.
In sum, the approach by Fisher, significance testing, involves specifying a statistical model, and using the p-value to test the assumptions of the model, such as specific values for μ or σ. If the p-value is smaller than the criterion value, either something improbable occurred or the null-hypothesis is not true. Crucially, the p-value may provide inductive evidence against the assumptions of the null-hypothesis, but a large p-value (larger than the criterion value) is not inductive support for the null-hypothesis.


Neyman-Pearson hypothesis testing

In contrast to Fisher’s evidential approach, Neyman and Pearson’s hypothesis testing is non-evidential.  Its primary goal is to choose on the basis of repeated random sampling between two hypotheses (or more; but I will only consider two)  in order to make behavioral decisions (so to speak) that will minimize decision errors and their associated costs (loss) in the long run. In stead of trying to figure out which of the two hypotheses is true, one decides to accept  one (and reject the other) of the two hypothesis as if it were true, without actually having to believe it, and act accordingly. 
As with Fisher, Neyman-Pearson hypothesis testing starts with formulating descriptive models of the population. We may for instance propose (after thinking and judging) that one model (hypothesis H1) assumes that the variable has a normal distribution with μ = 100 and one model (hypothesis H2) that assumes that the variable has a normal distribution with μ = 106.  We will assume the value of σ is known, say it equals 15.  We will have to choose one of the two hypothesis, by rejecting one (and accepting the other).

Let’s suppose that only one of the models is true and that they cannot both be false. This means that we can incorrectly decide to reject or accept each of the two hypotheses.  That is, if we incorrectly reject H1, we incorrectly accept H2. So, there are two types of errors we can make. A type I error occurs when we incorrectly reject a true hypothesis and a type II error occurs when we incorrectly accept a false hypothesis.

In a previous post (here), I used the following conceptual descriptions of these errors: the type I error is the error of excessive skepticism, and the type II error is the error of  extreme gullibility, but from the perspective of Neyman-Pearson hypothesis testing these conceptual descriptions may not make much sense, because these terms imply a relation between the decisions about a hypothesis and belief in the hypothesis, while in the Neyman-Pearson approach a rejection or non-rejection does not lead to commitment in believing or not believing the hypothesis, although the hypotheses themselves are based on beliefs (and judging and reasoning) that the descriptive model is suitable for the population at hand. 
The crucial point is that the goal of Neyman-Pearson hypothesis testing is to base courses of action on the decision to reject or not-reject a statistical hypothesis. This entails minimizing the costs (loss) associated with type I and type II errors. In particular, the approach minimizes the probability (β) of a type II error bounded by the probability (α) of a type I error. We may also say that we want to maximize the probability (1 – β), the probability of rejecting a false hypothesis, the so called power of the test, while keeping α at a maximum (usually low) value. 
Suppose, that our considerations of the loss associated with type I and type II errors, has led us to the insight that false rejection of  H2 is the most costly error. And suppose that we have agreed/determined/reasoned/judged that the probability of falsely rejecting it should be at most .05. So, α = .05. Of course, we also  “know” the loss associated with falsely accepting it, and we have determined that the probability β should not exceed .10. Now, suppose that we repeatedly sample N = 225 observations from the (unknown) population. We do not know whether H1 or H2 provides the correct description of the population, but we assume that one of them must be true if we select a particular sample, and they cannot both be false.

We will reject H2 (Normal distribution with μ = 106, and σ = 15) if the sample mean in our random sample equals 104.35 or less (this corresponds to a test statistic with value -1.65).  Why, because the probability of obtaining a sample mean equal or smaller than 104.35 is approximately .05 when H2 is true. Thus, if we repeatedly sample from the population when H2 is true, we will incorrectly reject it in 5% of the cases. Which is the probability of a type I error that we want.

We have arranged things so, that when H2 is false, H1 is per definition true. If H1 is true (H2 is false), there is a probability of approximately .99 to obtain a sample mean of 104.35 or smaller. Thus, the probability to reject H2 when it it false is .99, this is the power of the test, and the probability is approximately .01 of incorrectly not rejecting H2 when it is false. The latter probability is the probability of a type II error, which we did not want to be larger than .10.

Now suppose the results is that the sample mean equals 103 (the value of the test statistic equals -3). According to the decision criterion we reject H2 (with α = .05) and accept H1 and act as if μ = 100 is true. Crucially, we do not have to believe it is actually true, nor do we consider the test statistic with value -3 as inductive evidence against H2. So, the test result provides neither support for H1 nor evidence against H2, but we know from the specification of the models and the assumptions about sampling that repeatedly using this procedure leads to 5% type I errors and 1%  type II errors in the long run, depending on which of the two hypotheses is true (which is unknown to us).  Given that we know the loss associated with each error, we are able to minimize the expected loss associated with acting upon the decisions we make about the hypotheses.

Note that Fisher’s significance testing would consider the p-value associated with the test statistic of -3, i.e. p < .01 either as inductive evidence against H2 or as an indication that something unusual (improbable) happened assuming H2 is true. Note also that in Fisher’s approach, it is not possible to reason from the inferred untruth of H2 to the truth of H1, because H1 does not exist in that approach.

It should be noted further that in the Neyman-Pearson approach, the importance of the value of the test statistic is restricted to whether or not the value exceeds a critical value (i.e. whether or not the value of the statistic is in the rejection region). That means that it is of no concern how much the test statistic exceeds the critical value, since all values larger than the critical value lead to the same decision: reject the hypothesis. In other words, because the approach is non-evidential, the magnitude of the test statistic is inconsequential as far as the truth of the hypothesis is concerned. Compare this to the Fisher approach, where the larger the test statistic is (the smaller the p-value), the stronger the inductive evidence is against the null-hypothesis.

Null-hypothesis significance testing (NHST)

NHST combines Fisher’s significance testing with Neyman-Pearson hypothesis testing, without regard for the logical incompatibilities of the two approaches. Fisher’s p-value is used both as a measure of inductive evidence against the null-hypothesis, with smaller p-values considered to be stronger evidence against the null than larger p-values, and as a test statistic. In its latter use, the null-hypothesis is (usually) rejected if the p-value is smaller than .05.

Contrary to significance testing, NHST uses the p-value to decide between the null-hypothesis and an alternative hypothesis. But contrary to the Neyman-Pearson approach, α, the probability of a type I error is not based on judgement and careful consideration of loss-functions, but is mechanically set at .05 (or .01). And, contrary to the Neyman-Pearson approach, the probability of a type II error (β) is usually not considered.

One reason for the latter may be that specification of the null-hypothesis is also mechanized.  In the case of differences between means or testing correlations or regression coefficients, etc, the standard null-hypothesis is that the difference, the correlation or the coefficient equals 0. This is also called the nil-hypothesis. As the alternative excludes the null, the standard alternative hypothesis is that the parameter in question is not equal to zero, which makes it hard to say something about the type II error, because determining the probability of a type II error requires thinking about a minimal consequential effect size (consequential in terms of decisions and associated loss) that can serve as the alternative hypothesis.

Specifying a non-nil alternative hypothesis, i.e. that the parameter value is not equal to zero, implies that results arbitrarily close to nil, but not equal to nil, are as consequential as effect sizes that are far away from the null-value, both in acting upon the value as in not-acting upon it. Crucially, not specifying a minimal consequential effect size, rules out determining  β. So, even though NHST uses the concept of an alternative hypothesis (contrary to Fisher), the nil-hypothesis is such that the procedure of Neyman and Pearson can no longer work: it is impossible to strike a balance between loss associated with type I and type II errors, and so NHST is not a hypothesis testing procedure.

For these reasons I am very much inclined to characterize NHST as fixed-α significance testing. But using fixed-α in combination with an evidential interpretation of p-values leads to logical inconsistencies. (As always, I assume that being logically consistent is one of the characteristics of doing science, but maybe you disagree). Note, by the way, that I am talking about the p-value as measure of evidence against the nil-hypothesis, and not about the p-value as test statistic. (But remember that proper use of the p-value as test statistic requires being able to specify a non-nil alternative hypothesis). 
One of the logical inconsistencies is that α and the p-value-as-evidence involve contradictory conceptualisations of probability.  In terms of p-values, α is simply the probability that the p-value is smaller than .05 (the usual criterion) assuming the nil-hypothesis is true. That probability follows deductively from the specification of the null-hypothesis (including, of course,  the statistical model underlying it). Note that α is completely independent of actually realized results: it an assertion about the p-value assuming repeated sampling from the null-population; α is about the test-procedure and not about actual data.
But the p-value-as-evidence against the null is not the result of deductive reasoning, but of inductive reasoning. The p-value is not a probability associated with the test-procedure. It is a random variable the value of which depends on the actual data, the null-value and the statistical model. Crucially, from a single realized result (a p-value) an inference is made about a probability distribution. But this is inconsistent with the frequency interpretation of probability that underlies the conceptualisation of α, because under this interpretation no probability statement can be made about realized single results (except that the probability is 100% that it happened) or about an unrealized single result (that probability is 0 if it does not happen or 1.0 if it happens).  To make the point: using p-value-as-evidence and (fixed)-α requires both believing that probability statements can be made on the basis of a single result and believing that that is impossible.  So, it boils down to believing that both A and not-A are true. 
To me, logical inconsistencies like these disqualify NHST as a scientific means of statistical inference. I repeat that this is because I believe that doing science entails being logically consistent. Assuming or believing that A and not-A are both true, is not an example of logical consistency.

Scientific with a small s

My inspiration for this blog’s motto comes from Zilliak & McCloskey (2004). They quote from Bob Solow’s Nobel Prize acceptance speech, after which they write:

“Solow recommends we “try very hard to be scientific with a small s”; but the authors we have surveyed in the AER [American Economic Review, GM], by contrast, are trying to be scientific with a small t.” (p. 544).

Their “small t” refers to the t statistic on the basis of which researchers determine the p-values they use to assess the statistical significance of their findings. A small p (smaller than .05) is usually taken to mean that the test result is statistically significant.

There are a lot of reasons to believe that null-hypothesis significance testing (NHST) is basically unscientific. That’s why I got convinced that you cannot do science with a small p (significance testing). I hope that after reading the blog posts yet to come, you will be convinced as well.  (If you can’t wait: Kline (2014) (see below) is a good place to start getting convinced).

What does it mean to be scientific with a small s? To Solow (as cited in Zilliak & McCloskey, 2004) it simply means thinking logically and respecting the facts.  To my mind, thinking logically as a prerequisite of being scientific (with a small s) includes thinking logically about the results of statistical analyses. For instance, that you should not mistakenly believe that a small p value means that it is unlikely that a result is due to chance, or that you should not mistakenly believe that the long term behavior of a decision procedure has anything to do with the evidence in your actual data (the facts).

Zilliak & McCloskey (2004) write about economic research, but significance testing is of course not limited to economic research. Kline (2013, p. 118-199) concludes in his chapter about cognitive distortions in significance testing (and he is putting it mildly):

“Significance testing has been like a collective Rorschach inkblot test for the behavioral sciences: What we see in it has more to do with wish fulfillment than reality. This magical thinking has impeded the development of psychology and other disciplines as cumulative sciences. […] the gap between what is required for significance tests to be accurate and characteristics of real world studies is just too great.”

So, this blog is about being scientific with a small s, with a main focus on the logic and illogic of NHST, because you simply cannot do science with only a small p.

Kline, R.B. (2013). Beyond significance testing. Statistics reform in the behavioral sciences. Second Edition. Washington: APA.
Zilliak, S.T., & McCloskey, D.N. (2004). Size matters: the standard error of regressions in the American Economic Review, Journal of Socio-Economics, 33, 527-547.