Do Frequent prayer and PISA science go together across countries?

Across 51 countries the link before adjusting for anything is r = -0.75, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.76.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does Frequent prayer cause PISA science?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Frequent prayer vs. PISA science

Holds up after adjusting for income

A country is not a person. Countries where more people are counted as belonging to this group also tend to differ on the other measure, and that is all. It does not follow that the people in the group are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. It is the most common misuse of this kind of data. Religion also varies strongly with region, history and national income, so much of what looks religious here is really those. That is what the adjusted figures are for. The religion shares are estimates for two years only, and the survey questions are asked in rounds that can be ten years apart.

Across 51 countries, Frequent prayer and PISA science rise and fall together at r = -0.75 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.57 (q = 0.018, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains.

Comparing countries across the middle half of frequent prayer, 26% to 72.9% of respondents, pisa science is worse by 74.3 PISA points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 36.6 PISA points.

Link before adjusting (r)-0.748p = 2.7e-10 · 51 countries

After adjusting for national income-0.569p = 1.6e-5 · likely range [-0.73, -0.34]

p, allowing for regions0.001standard error 0.062 · 5 regions

Check for luck (q)0.018across 12,497 pairs

How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.

Frequent prayer · % of respondentslink with income -0.68 · high

PISA science · PISA pointslink with income +0.78 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.748	2.7e-10	51
By rank (Spearman), not adjusted	-0.756	1.4e-10	51
Standard (Pearson), adjusted for national income	-0.569	1.6e-5	51
By rank (Spearman), adjusted for national income	-0.553	3.1e-5	51

The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 51 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.180 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.603 (without SGP) to -0.507 (without VNM). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: VNM (+0.062).

Within each region

Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.

	n	r
East Asia & Pacific	13	-0.70
Europe & Central Asia	21	-0.62

By income third

If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.

	n	r
Poorest third	17	-0.74
Middle third	17	-0.58
Richest third	17	-0.56

By largest religious group

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

	n	r
Christians	28	-0.66

Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = -0.569 after dropping 0 countries.

Two series, one country

Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data

	% of respondents	PISA points	Years
Trinidad and Tobago TTO	96.8%	425	2014, 2015
Indonesia IDN	95.3%	383	2022
Philippines PHL	92.4%	356	2022
West Bank and Gaza PSE	90.5%	369	2014, 2022
Colombia COL	87.9%	411	2022
Jordan JOR	86.9%	375	2022
Algeria DZA	81.8%	376	2014, 2015
Morocco MAR	80.9%	365	2022
Lebanon LBN	80.9%	384	2022, 2018
Malaysia MYS	79.7%	416	2022
Brazil BRA	77.4%	403	2022
Tunisia TUN	76.5%	386	2022, 2015
Guatemala GTM	73.2%	373	2022
Uzbekistan UZB	72.6%	355	2022
Mexico MEX	71.9%	410	2022

Haerpfer, C. et al., World Values Survey; EVS, European Values Study
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

Sources

Frequent prayer

Share praying at least several times a week

World Values Survey Association and European Values Study · via Our World in Data · Free for non-commercial use with attribution · updated irregular (survey waves, roughly every 5 years)

Private practice. Attendance is public practice, and importance is belief. The three can differ: a country can pray often and attend rarely. Asked only from the 2014 wave on, so it covers about eighty countries. No "better" direction is set, for the same reason as attendance.

PISA science

PISA mean science score

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator

How this was computed

Data used: most recent value in 2010-2025; values come from 2014–2022. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 5 World Bank regions. The q-value corrects for testing all 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Frequent prayer and PISA science

This page shows that two measures go together. It does not show that Frequent prayer changes PISA science, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.