Dennis Lindley

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Dennis Lindley, one of my many heroes, was an English statistician, decision theorist and leading advocate of Bayesian statistics. He published a pivotal book, Understanding Uncertainty, that changed my view on what is and how to handle uncertainty in a coherent1 way. He is responsible for one of my favorites quotes: “Inside every non-Bayesian there is a Bayesian struggling to get out”; and one of my favorite heuristics around prior probabilities: Cromwell’s Rule2. Lindley predicted in 1975 that “Bayesian methods will indeed become pervasive, enabled by the development of powerful computing facilities” (Lindley, 1975). You can find more about all of Lindley’s achievements in his obituary.

Lindley’s Paradox

Lindley’s paradox3 is a counterintuitive situation in statistics in which the Bayesian and frequentist approaches to a hypothesis testing problem give different results for certain choices of the prior distribution.

More formally, the paradox is as follows. We have some parameter $\theta$ that we are interested in. Then, we proceed with an experiment to test two competing hypotheses:

  1. $H_0$ (also known as null hypothesis): there is no “effect”, or, more specifically, $\theta = 0$.
  2. $H_a$ (also known as alternative hypothesis): there is an “effect”, or, more specifically, $\theta \ne 0$.

The paradox occurs when two conditions are met:

  1. The result of the experiment is significant by a frequentist test of $H_0$, which indicates sufficient evidence to reject $H_0$, at a certain threshold of probability4.
  2. The posterior probability (Bayesian approach) of $H_0 \mid \theta$ (null hypothesis given $\theta$) is high, which indicates strong evidence that $H_0$ should be favored over $H_a$, that is, to not reject $H_0$.

These results can occur at the same time when $H_0$ is very specific, $H_a$ more diffuse, and the prior distribution does not strongly favor one or the other. These conditions are pervasive across science and common in traditional null-hypothesis significance testing approaches.

This is a duel of frequentist versus Bayesian approaches, and one of the many in which Bayesian emerges as the most coherent. Let’s give a example and go over the analytical result with a ton of math, but also a computational result with Julia.


Here’s the setup for the example. In a certain city 49,581 boys and 48,870 girls have been born over a certain time period. The observed proportion of male births is thus $\frac{49,581}{98,451} \approx 0.5036$.

We assume that the birth of a child is independent with a certain probability $\theta$. Since our data is a sequence of $n$ independent Bernoulli trials, i.e., $n$ independent random experiments with exactly two possible outcomes: “success” and “failure”, in which the probability of success is the same every time the experiment is conducted. We can safely assume that it follows a binomial distribution with parameters:

  • $n$: the number of “trials” (or the total number of births).
  • $\theta$: the probability of male births.

We then set up our two competing hypotheses:

  1. $H_0$: $\theta = 0.5$.
  2. $H_a$: $\theta \ne 0.5$.

Analytical Solution

This is a toy-problem and, like most toy problems, we can solve it analytically5 for both the frequentist and the Bayesian approaches.

Analytical Solutions – Frequentist Approach

The frequentist approach to testing $H_0$ is to compute a $p$-value4, the probability of observing births of boys at least as large as 49,581 assuming $H_0$ is true. Because the number of births is very large, we can use a normal approximation6 for the binomial-distributed number of male births. Let’s define $X$ as the total number of male births, then $X$ follows a normal distribution:

$$X \sim \text{Normal}(\mu, \sigma)$$

where $\mu$ is the mean parameter, $n \theta$ in our case, and $\sigma$ is the standard deviation parameter, $\sqrt{n \theta (1 - \theta)}$. We need to calculate the conditional probability of $X \geq \frac{49,581}{98,451} \approx 0.5036$ given $\mu = n \theta = 98,451 \cdot \frac{1}{2} = 49,225.5$ and $\sigma = \sqrt{n \theta (1 - \theta)} = \sqrt{98,451 \cdot \frac{1}{2} \cdot (1 - \frac{1}{2})}$:

$$P(X \ge 0.5036 \mid \mu = 49,225.5, \sigma = \sqrt{24.612.75})$$

This is basically a cumulative distribution function (CDF) of $X$ on the interval $[49,225.5, 98,451]$:

$$\int_{49,225.5}^{98,451} \frac{1}{\sqrt{2 \pi \sigma^2}} e^{- \frac{\left( \frac{x - \mu}{\sigma} \right)^2}{2}} dx$$

After inserting the values and doing some arithmetic, our answer is approximately $0.0117$. Note that this is a one-sided test, since it is symmetrical, the two-sided test would be $0.0117 \cdot 2 = 0.0235$. Since we don’t deviate from the Fisher’s canon, this is well below the 5% threshold. Hooray! We rejected the null hypothesis! Quick! Grab a frequentist celebratory cigar! But, wait. Let’s check the Bayesian approach.

Analytical Solutions – Bayesian Approach

For the Bayesian approach, we need to set prior probabilities on both hypotheses. Since we do not favor one from another, let’s set equal prior probabilities:

$$P(H_0) = P(H_a) = \frac{1}{2}$$

Additionally, all parameters of interest need a prior distribution. So, let’s put a prior distribution on $\theta$. We could be fancy here, but let’s not. We’ll use a uniform distribution on $[0, 1]$.

We have everything we need to compute the posterior probability of $H_0$ given $\theta$. For this, we’ll use Bayes theorem7:

$$P(A \mid B) = \frac{P(B \mid A) P(A)}{P(B)}$$

Now again let’s plug in all the values:

$$P(H_0 \mid \theta) = \frac{P(\theta \mid H_0) P(H_0)}{P(\theta)}$$

Note that by the axioms of probability and by the product rule of probability we can decompose $P(\theta)$ into:

$$P(\theta) = P(\theta \mid H_0) P(H_0) + P(\theta \mid H_a) P(H_a)$$

Again, we’ll use the normal approximation:

$$ \begin{aligned} &P \left( \theta = 0.5 \mid \mu = 49,225.5, \sigma = \sqrt{24.612.75} \right) \\ &= \frac{ \frac{1}{\sqrt{2 \pi \sigma^2}} e^{- \left( \frac{(\mu - \mu \cdot 0.5)}{2 \sigma} \right)^2} \cdot 0.5 } { \frac{1}{\sqrt{2 \pi \sigma^2}} e^{ \left( -\frac{(\mu - \mu \cdot 0.5)}{2 \sigma} \right)^2} \cdot 0.5 + \int_0^1 \frac {1}{\sqrt{2 \pi \sigma^2} } e^{- \left( \frac{\mu - \mu \cdot \theta)}{2 \sigma} \right)^2}d \theta \cdot 0.5 } \\ &= 0.9505 \end{aligned} $$

The likelihood of the alternative hypothesis, $P(\theta \mid H_a)$, is just the CDF of all possible values of $\theta \ne 0.5$.

$$P(H_0 \mid \text{data}) = P \left( \theta = 0.5 \mid \mu = 49,225.5, \sigma = \sqrt{24.612.75} \right) > 0.95$$

And we fail to reject the null hypothesis, in frequentist terms. However, we can also say in Bayesian terms, that we strongly favor $H_0$ over $H_a$.

Quick! Grab the Bayesian celebratory cigar! The null is back on the game!

Computational Solutional

For the computational solution, we’ll use Julia and the following packages:

Computational Solutions – Frequentist Approach

We can perform a BinomialTest with HypothesisTest.jl:

julia> using HypothesisTests

julia> BinomialTest(49_225, 98_451, 0.5036)
Binomial test
Population details:
    parameter of interest:   Probability of success
    value under h_0:         0.5036
    point estimate:          0.499995
    95% confidence interval: (0.4969, 0.5031)

Test summary:
    outcome with 95% confidence: reject h_0
    two-sided p-value:           0.0239

    number of observations: 98451
    number of successes:    49225

This is the two-sided test, and I had to round $49,225.5$ to $49,225$ since BinomialTest do not support real numbers. But the results match with the analytical solution, we still reject the null.

Computational Solutions – Bayesian Approach

Now, for the Bayesian computational approach, I’m going to use a generative modeling approach, and one of my favorites probabilistic programming languages, Turing.jl:

julia> using Turing

julia> @model function birth_rate()
           θ ~ Uniform(0, 1)
           total_births = 98_451
           male_births ~ Binomial(total_births, θ)

julia> model = birth_rate() | (; male_births = 49_225);

julia> chain = sample(model, NUTS(1_000, 0.8), MCMCThreads(), 1_000, 4)
Chains MCMC chain (1000×13×4 Array{Float64, 3}):

Iterations        = 1001:1:2000
Number of chains  = 4
Samples per chain = 1000
Wall duration     = 0.2 seconds
Compute duration  = 0.19 seconds
parameters        = θ
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse    ess_bulk    ess_tail      rhat   ess_per_sec
      Symbol   Float64   Float64   Float64     Float64     Float64   Float64       Float64

           θ    0.4999    0.0016    0.0000   1422.2028   2198.1987    1.0057     7368.9267

  parameters      2.5%     25.0%     50.0%     75.0%     97.5%
      Symbol   Float64   Float64   Float64   Float64   Float64

           θ    0.4969    0.4988    0.4999    0.5011    0.5031

We can see from the output of the quantiles that the 95% quantile for $\theta$ is the interval $(0.4969, 0.5031)$. Although it overlaps zero, that is not the equivalent of a hypothesis test. For that, we’ll use the highest posterior density interval (HPDI), which is defined as “choosing the narrowest interval” that captures a certain posterior density threshold value. In this case, we’ll use a threshold interval of 95%, i.e. an $\alpha = 0.05$:

julia> hpd(chain; alpha=0.05)
  parameters     lower     upper
      Symbol   Float64   Float64

           θ    0.4970    0.5031

We see that we fail to reject the null, $\theta = 0.5$ at $\alpha = 0.05$ which is in accordance with the analytical solution.

Why the Frequentist and Bayesian Approaches Disagree

Why do the approaches disagree? What is going on under the hood?

The answer is disappointing8. The main problem is that the frequentist approach only allows fixed significance levels with respect to sample size. Whereas the Bayesian approach is consistent and robust to sample size variations.

Taken to extreme, in some cases, due to huge sample sizes, the $p$-value is pretty much a proxy for sample size and have little to no utility on hypothesis testing. This is known as $p$-hacking9.


This post is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.



Lindley, Dennis V. “The future of statistics: A Bayesian 21st century”. Advances in Applied Probability 7 (1975): 106-115.

  1. as far as I know there’s only one coherent approach to uncertainty, and it is the Bayesian approach. Otherwise, as de Finetti and Ramsey proposed, you are susceptible to a Dutch book. This is a topic for another blog post… ↩︎

  2. Cromwell’s rule states that the use of prior probabilities of 1 (“the event will definitely occur”) or 0 (“the event will definitely not occur”) should be avoided, except when applied to statements that are logically true or false. Hence, anything that is not a math theorem should have priors in $(0,1)$. The reference comes from Oliver Cromwell, asking, very politely, for the Church of Scotland to consider that their prior probability might be wrong. This footnote also deserves a whole blog post… ↩︎

  3. Stigler’s law of eponymy states that no scientific discovery is named after its original discoverer. The paradox was already was discussed in Harold Jeffreys' 1939 textbook. Also, fun fact, Stigler’s is not the original creator of such law… Now that’s a self-referential paradox, and a broad version of the Halting problem, which should earn its own footnote. Nevertheless, we are getting into self-referential danger zone here with footnotes’ of footnotes’ of footnotes’… ↩︎

  4. this is called $p$-value and can be easily defined as “the probability of sampling data from a target population given that $H_0$ is true as the number of sampling procedures $\to \infty$”. Yes, it is not that intuitive, and it deserves not a blog post, but a full curriculum to hammer it home. ↩︎ ↩︎

  5. that is not true for most of the real-world problems. For Bayesian approaches, we need to run computational asymptotic exact approximations using a class of methods called Markov chain Monte Carlo (MCMC). Furthermore, for some nasty problems we need to use different set of methods called variational inference (VI) or approximate Bayesian computation (ABC)↩︎

  6. if you are curious about how this approximation works, check the backup slides of my open access and open source graduate course on Bayesian statistics↩︎

  7. Bayes’ theorem is officially called Bayes-Price-Laplace theorem. Bayes was trying to disprove David Hume’s argument that miracles did not exist (How dare he?). He used the probabilistic approach of trying to quantify the probability of a parameter (god exists) given data (miracles happened). He died without publishing any of his ideas. His wife probably freaked out when she saw the huge pile of notes that he had and called his buddy Richard Price to figure out what to do with it. Price struck gold and immediately noticed the relevance of Bayes’ findings. He read it aloud at the Royal Society. Later, Pierre-Simon Laplace, unbeknownst to the work of Bayes, used the same probabilistic approach to perform statistical inference using France’s first census data in the early-Napoleonic era. Somehow we had the answer to statistical inference back then, and we had to rediscover everything again in the late-20th century… ↩︎

  8. disappointing because most of published scientific studies suffer from this flaw. ↩︎

  9. and, like all footnotes here, it deserves its own blog post… ↩︎