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Why is Hydration important

On the surface it might appear that this topic does not have a lot to do with statistics and science. This however changes when you take a closer look. It has a lot to do with food science and human nutrition, although hydration is still part of psychological research. Therefore I encourage you to carry on reading.

The water makes up to 60% of the human body weight (Guyto ,1976). Therefore it is extremely important to keep the fluid level in the body at a right level. Only 1% of dehydration (1), can cause performance to drop by even 10%. This includes decreased physical and mental performance, decreased concentration, headaches, increased heart rate, increase/ decrease of body temperature and discomfort. People who exercise while dehydrated put themselves at risk of different kinds of injuries.

Other than general well being water also have some long term impact on human body’s. The most obvious is that it filtrates the body, especially the kidneys. It is a perfect way for the organism to get rid of wastes and bacteria that can cause different diseases. It has been shown that regular consumption of water helps to prevent cancers (2). On top of this water plays a key role in the digestion system. Keeping the right level of fluid in the organism will make the digestion system to work better, will decrease the feeling of hunger (which will result in eating less undesirable food), and

There is also a downside to drinking water. One of them is having to visit toilet frequently (which is a real pain because I need to make a brake every 20 minutes when writing this blog after I had 5 liters of water so far today…), and the other one is a condition called hyponatremia. It is a result of drinking too much water, which causes the sodium in the body to become diluted, and causes the cells to start swelling.

In their study, Ciab et. al, tested 8 healthy endurance trained men. Their conditions were: euhydrated (normal state of body water content), dehydrated (up to a weight loss of 2.8%), and hyperhydrated (with a solution containing glycerol having ingested volume equal to 21.4 ml/kg of body weight). The subjects were assigned to a pedaling exercise and were administered psychological testing of psycho-motor skill, perceptive discrimination, fatigue, memory and mood. The dehydration conditions impaired cognitive abilities. After an arm crank exercise, further effects of dehydration were identified for tracking performance. Also long term memory was impaired in hydration and control situations, but no decrement in performance in hyperhydration group.

There are countless benefits of drinking water however I am a little restricted by time, and the number of words I put in this blog, as nobody would probably read it. The simplest conclusion for this topic therefore is: Want to feel better? Drink 2 to 3 liters of water a day.


Guyton, Arthur C. (1976). Textbook of Medical Physiology (5th ed.). Philadelphia: W.B. Saunders. p. 424.

‘Foods, Nutrition and Sports Performance,’ ed. C. Williams and J.T. Devlin (1994), pp 147-178

‘Clinical Sports Nutrition,’ ed. L. Burke and V. Deakin (1994), pp 333-364

E.F. Coyle (1994). ‘Fluid and carbohydrate replacement during exercise: how much and why?.’ Sports Science Exchange, 50, vol. 7, no. 3

Maughan, Leiper & Shirreffs (1996). ‘Rehydration and recovery after exercise.’ Sports Science Exchange, 62, vol. 9, no. 3

C. Cian., N. Koulmann., P. Barraud., C. Raphel., C. Jimenez., B. Melin,. (2000)., Influences of variations in body hydration on cognitive function: Effect of hyperhydration, heat stress, and exercise-induced dehydration. Journal of Psychophysiology, Vol 14(1), 2000, 29-36. doi: 10.1027//0269-8803.14.1.29




Dear Shanti, here are the links to the comments I have made for this week:





The Simpson’s Paradox

This statistical paradox has been introduced by E.H. Simpson in 1951. This apparently impossible effect can be very often observed in social and medical research. The idea is that the effect of several groups seems reversed when the groups are combined.

As a real world example of this paradox, it is worth looking at the example proposed by Morrel (1999)[ http://www.amstat.org/publications/jse/secure/v7n3/datasets.morrell.cfm%5D, or Charig, Webb, Payne, and Wickham (1986). In this medical study they compared success rates on two different treatments for kidney stones. 

The following table shows the success rates and the number of treatments involving both large and small kidney stones. Treatment A includes all the open surgery procedures, and treatment B percutaneous nephrolithotomy.


Treatment A:

Treatment B:

Small Stones:

Group 1

93% (81/87)

Group 2

87% (234/270)

Large Stones:

Group 3

73% (192/263)

Group 4

69% (55/80)


78% (273/350)

83% (289/350)


As a conclusion it is clear that the treatment A is more effective than the treatment B on Small stones as well as on the Large stones. However when both sizes are compared at the same time, the treatment B seems to be more effective.

I am sure many of you can instantly see a big problem with this design. The group sizes are not equal. Also the size of the stone was a confounding variable. The inequality between the two ratios (success/ total), needs to be considered to determine which treatment is more successful.

When the confounding variable is ignored, the group means differ a lot. The severe cases (large stones) were more likely to receive the better treatment (A), and milder cases (small stones) the other treatment. So the totals were dominated by bigger groups 1, and 3, rather than smaller 2, and 4. 

Severity of the case, influenced the success rate more than the choice of the treatment. This means that the group of patients with small stones (group 2) will do still do worse when administered the inferior treatment (B), than patients with large stones (group 3) when administered the better treatment.

According to Pavlides and Perlman (2009), the probability of occurrence of the Simpson’s Paradox just by chance in a 2x2x2 table is 1/60.

The Simpson’s Paradox points out how important it is to include data about possible confounding variables, when calculating causal relations. Pearl (2000, 2009), gives a precise criteria for selecting confounding variables when using causal graphs.

In conclusion this is another very interesting probability paradox. The researchers need to be very careful, and try to avoid such confounding variables. Not just to avoid getting involved with the paradox itself but also to avoid making Type 1 and Type 2 errors which are often results of confounding variables. However it might be a good idea to explore this topic further for the next blog. Thanks for reading!

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