James Lind (1753), A treatise of the scurvy – key excerpts

Excerpts from the Lind (1753), with help on the ye olde English from others who have quoted him (Hughes, 1975; Bartholomew, 2002; Weber & De Vreese, 2005).

Lind’s study is sometimes presented as an RCT, but it’s not clear how his patients were assigned to groups, just that the cases “were as similar as I could have them” (see discusison in Weber & De Vreese, 2005). Bartholomew (2002) argues that Lind was convinced scurvy was a disease of the digestive system and warns against quoting the positive outcomes for oranges and lemons (and cider) out of the broader context of Lind’s other work.

Here’s what Lind said he did:

“On the 20th May, 1747, I took twelve patients in the scurvy on board the Salisbury at sea. Their cases were as similar as I could have them. They all in general had putrid gums, the spots and lassitude, with weakness of their knees. They lay together in one place, being a proper apartment for the sick in the fore-hold; and had one diet in common to all, viz., water gruel sweetened with sugar in the morning; fresh mutton broth often times for dinner; at other times puddings, boiled biscuit with sugar etc.; and for supper barley, raisins, rice and currants, sago and wine, or the like.”

Groups (n = 2 in each):

  • “ordered each a quart of cyder a day”
  • “twenty five gutts of elixir vitriol three times a day upon an empty stomach, using a gargle strongly acidulated with it for their mouths.”
  • “two spoonfuls of vinegar three times a day upon an empty stomach”
  • “a course of sea water”
  • “two oranges and one lemon given them every day. These they eat with greediness”
  • “The two remaining patients took the bigness of a nutmeg three times a day of an electuray recommended by an hospital surgeon made of garlic, mustard seed, rad. raphan., balsam of Peru and gum myrrh, using for common drink barley water well acidulated with tamarinds, by a decoction of which, with the addition of cremor tartar, they were gently purged three or four times during the course”

Excerpt from the study outcomes:

  • “The consequence was that the most sudden and visible good effects were perceived from the use of the oranges and lemons; one of those who had taken them, being at the end of six days fit for duty”
  • “Next to the oranges, I thought the cyder had the best effects”


Bartholomew, M. (2002). James Lind’s Treatise of the Scurvy (1753). Postgraduate Medical Journal, 78, 695–696.

Hughes, R. E. (1975). James Lind and the cure of Scurvy: An experimental approach. Medical History, 19(4), 342–351.

Weber, E., & De Vreese, L. (2005). The causes and cures of scurvy. How modern was James Lind’s methodology? Logic and Logical Philosophy, 14(1), 55–67.

maggie and milly and molly and may

maggie and milly and molly and may
went down to the beach(to play one day)

and maggie discovered a shell that sang
so sweetly she couldn’t remember her troubles,and

milly befriended a stranded star
whose rays five languid fingers were;

and molly was chased by a horrible thing
which raced sideways while blowing bubbles:and

may came home with a smooth round stone
as small as a world and as large as alone.

For whatever we lose(like a you or a me)
it’s always ourselves we find in the sea

E. E. Cummings

No one else can feel it for you

“I want to know what it is like for a bat to be a bat. Yet if I try to imagine this, I am restricted to the resources of my own mind, and those resources are inadequate to the task” (Nagel, 1974).

“Feel the rain on your skin, no one else can feel it for you” (Bedingfield, 2004).

Software crisis

History has valuable lessons on AI. Many of us are aware of the replication crisis in social science. Were you aware of the software crisis, first famously discussed at the NATO Conference on Software Engineering in Garmisch, October 1968? We have got used to software being buggy, updates being required on a near-daily basis, often to fix security vulnerabilities – and, given the vast number of high profile cyber attacks, often too late. People are now suggesting using large language models, trained on code people have dumped on the web, to write software. Software testing and static program analysis are going to be more important than ever, whether you’re evaluating internet-connected apps or statistical analysis code.

The original reports are available online. It’s worth having a browse around to see the issues. In 1968, hardware and software had a tiny fraction of the computational and political power it has now.

The Bletchley Declaration

Worth a read. One key para:

“We affirm that, whilst safety must be considered across the AI lifecycle, actors developing frontier AI capabilities, in particular those AI systems which are unusually powerful and potentially harmful, have a particularly strong responsibility for ensuring the safety of these AI systems, including through systems for safety testing, through evaluations, and by other appropriate measures.”

Prof Eve L. Ewing – “Why I capitalise White”

“Many Black people I know say that they capitalize Black as a show of respect, pride, and celebration, and they don’t want to afford the same courtesy to Whiteness. But we frequently capitalize words for reasons other than respect – words like Holocaust, or Hell […]. When we ignore the specificity and significance of Whiteness – the things that it is, the things that it does – we contribute to its seeming neutrality and thereby grant it power to maintain its invisibility.”
– Prof Eve L. Ewing (2020), I’m a Black Scholar Who Studies Race. Here’s Why I Capitalize ‘White.’

Terminology of programme theory in evaluation

This tickles me (Funnell & Rogers, 2011, pp. 23-24):

Over the years, many different terms have been used to describe the approach to evaluation that is based on a “plausible and sensible model of how the program is supposed to work” (Bickman, 1987b):

      • Chains of reasoning (Torvatn, 1999)
      • Causal chain (Hall and O’Day, 1971)
      • Causal map (Montibeller and Belton, 2006)
      • Impact pathway (Douthwaite et al., 2003)
      • Intervention framework (Ministry of Health, NZ 2002)
      • Intervention logic (Nagarajan and Vanheukelen, 1997)
      • Intervention theory (Argyris, 1970; Fishbein et al., 2001)
      • Logic model (Rogers, 2004)
      • Logical framework (logframe) (Practical Concepts, 1979)
      • Mental model (Senge, 1990)
      • Outcomes hierarchy (Lenne and Cleland, 1987; Funnell, 1990, 1997)
      • Outcomes line
      • Performance framework (Montague, 1998; McDonald and Teather, 1997)
      • Program logic (Lenne and Cleland, 1987; Funnell, 1990, 1997)
      • Program theory (Bickman, 1990)
      • Program theory-driven evaluation science (Donaldson, 2005)
      • Reasoning map
      • Results chain
      • Theory of action (Patton, 1997; Schorr, 1997)
      • Theory of change (Weiss, 1998)
      • Theory-based evaluation (Weiss, 1972; Fitz-Gibbon and Morris, 1975)
      • Theory-driven evaluation (Chen and Rossi, 1983)


Funnell, S. C., & Rogers, P. J. (2011). Purposeful Program Theory: Effective Use of Theories of Change and Logic Models. Jossey-Bass.

Data alone can’t determine causal structure

Suppose we find that the probability of a successful programme outcome (out) depends on treatment (treat) and mediator (med) as per the Bayes network depicted in part 1 of the figure below. Suppose also that there are no other unmeasured variables. This model defines \(P(\mathit{out} | \mathit{treat}, \mathit{med})\), \(P(\mathit{med} | \mathit{treat})\), and \(P(\mathit{treat})\). The arrows denote these probabilistic relationships.

Interpreting the arrows as causal relations, then all six models above are consistent with the conditional probabilities. Model 2 says that treatment and outcome are associated with each other because the mediator is a common cause. Model 3 says that the outcome causes treatment assignment. Model 4 says that the treatment causes mediator and outcome; however, outcome causes mediator. And so on. These six models are all members of the same Markov equivalence class (see Verma & Pearl, 1990).

We need something beyond the data and statistical assocations to distinguish between them: theory. Some of the theory might be trivial, e.g., that the outcome followed treatment and can’t have caused the treatment because we have ruled out time travel.


Verma, T., & Pearl, J. (1990). Equivalence and synthesis of causal models. Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, 255–270.