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[S] Scientific methodology

Science is an activity that consists in the explanation, prediction, and control of empirical phenomena in a rational manner. By "scientific reasoning" we mean the principles of reasoning relevant to the pursuit of this activity. They include principles governing experimental design, hypothesis testing, and the interpretation of data.

Scientific reasoning is important not just for institutional scientific research. It is true that scientists use specialized theories (e.g. quantum physics) which non-scientists do not have to use in everyday life. But many of the principles of reasoning (e.g. rules for identifying causes) are applicable also to everyday life. Even if we are not scientists, we need to make use of good reasoning to explain, predict, and control the events around us. When we want to jumpstart our career, protect our investments, improve our health, we need to gather evidence to find an effective way which is likely to achieve our aims. In all these cases, good scientific thinking skills help.

 Tutorials

· [S01] Theories & evidence

· [S02] Scientific method

· [S03] Theory choice

· [S04] Causation

· [S05] Mill's methods

· [S06] Causal inferences

· [S07] Causal diagrams

· [S08] Causal fallacies

· [S09] Scientific research

Reference: Lau, J., & Chan, J. (2017). Scientific methodology: Tutorials 1-9. https://philosophy.hku.hk/think/sci/

 Further readings

· Highly recommended: Theodore Schick Jr. and Lewis Vaughn -  How to Think About Weird Things: Critical Thinking for a New Age

· Philip Tetlock, Dan Gardner - Superforecasting: The Art and Science of Prediction

· Ronald N. Giere and others -  Understanding Scientific Reasoning

· Howson and Urbach -  Scientific Reasoning : The Bayesian Approach

[S01] Theories & evidence

There are lots of different branches of science, such as chemistry, biology, physics, etc. But generally speaking, there are four main components in scientific research :

1. Theories - These are the hypotheses, laws and facts about the empirical world.

2. The world - All the different objects, processes and properties of the universe.

3. Explanations and predictions - We use our theories to explain what is going on in the world, and to make predictions. Most predictions are about the future, but we can also have predictions about the past (retrodictions). For example, a geological theory about the earth's history might predict that certain rocks contains a high percentage of iron. A crucial part of scientific research is to test a theory by checking whether its predictions are correct or not.

4. Data (evidence) - The information that is gathered from observations or experiments. We use data to test our theories. They might also inspire new directions in research.

So in order to understand a scientific theory, we need to be able to say: (1) which are the laws, principles and facts included in the theory, (2) what do these theories tell us about the nature of the world, (3) what can it predict and what can it explain, and (4) what are the main pieces of evidence used to support the theory, and whether there might be evidence against the theory.

§1. Two misconceptions

Here are two misconceptions about science which seem rather common:

· Some people like to say that nothing can be proven in science, so it is all based on faith, just like religion. What is right about this view is that most scientific claims cannot be proven because scientists are not 100% certain. But this does not mean that accepting them is solely a matter of faith, because we can still have very strong evidence supporting them. In life, we have to be content with probability, not absolute certainty. We cannot prove with 100% certainty that you will die if you jump out of an airplane without a parachute. In fact quite a few people have survived. But of course it would be very stupid for you to try it just because there is no such proof.

· In science, a set of claims and principles about some particular subject matter is called a "theory". Sometimes this misleads some people because in ordinary language, "theory" is often used to talk about a tentative hypothesis with little evidence to support it. So now and then there are people who say things like "evolution is just a theory", "Einstein's theories are just that -- theories". These claims are not really helpful and not very clear. It is true that they are regarded as theories, but it does not mean they are speculative hypotheses on a par with any other wild guesses that people might come up with. A scientific theory can be a claim that is strongly supported by a wide range of evidence. Saying that it is "just" a theory would not be fair.

[S02] Scientific method

§1. The hypothetical-deductive method

The hypothetical-deductive method (HD method) is a very important method for testing theories or hypotheses. It is sometimes said to be "the scientific method". This is not quite correct because surely there is not just one method being used in science. However, it is true that the HD method is of central importance, because it is one of the more basic methods common to all scientific disciplines, whether it is economics, physics, or biochemistry. Its application can be divided into four stages:

§2. Example

Here is an illustration:

1. Suppose your portable music player fails to switch on. You might then consider the hypothesis that perhaps the batteries are dead. So you decide to test whether this is true.

2. Given this hypothesis, you predict that the music player should work properly if you replace the batteries with new ones.

3. So you proceed to replace the batteries, which is the "experiment" for testing the prediction.

4. If the player works again, then your hypothesis is confirmed, and so you throw away the old batteries. If the player still does not work, then the prediction is false, and the hypothesis is disconfirmed. So you might reject your original hypothesis and come up with an alternative one to test, e.g. the batteries are ok but your music player is broken.

§3. Some comments

The example above helps us illustrate a few points about science and the HD method.

1. A scientific hypothesis must be testable

The HD method tells us how to test a hypothesis, and a scientific hypothesis must be one that is capable of being tested.

If a hypothesis cannot be tested, we cannot find evidence to show that it is probable or not. In that case it cannot be part of scientific knowledge. Consider the hypothesis that there are ghosts which we cannot see and can never interact with, and which can never be detected either directly or indirectly. This hypothesis is defined in such a way to exclude the possibility of testing. It might still be true and there might be such ghosts, but we would never be in a position to know and so this cannot be a scientific hypothesis.

2. Confirmation is not truth

In general, confirming the predictions of a theory increases the probability that a theory is correct. But in itself this does not prove conclusively that the theory is correct.

To see why this is the case, we might represent our reasoning as follows :

If H then P. P. Therefore H.

Here H is our hypothesis "the batteries are dead", and P is the prediction "the player will function when the batteries are replaced". This pattern of reasoning is of course not valid, since there might be reasons other than H that also bring about the truth of P. For example, it might be that the original batteries are actually fine, but they were not inserted properly. Replacing the batteries would then restore the loose connection. So the fact that the prediction is true does not prove that the hypothesis is true. We need to consider alternative hypotheses and see which is more likely to be true and which provides the best explanation of the prediction. (Or we can also do more testing!)

In the next tutorial we shall talk about the criteria that help us choose between alternative hypotheses.

3. Disconfirmation need not be falsity

Very often a hypothesis generates a prediction only when given additional assumptions (auxiliary hypotheses). In such cases, when a prediction fails the theory might still be correct.

Looking back at our example again, when we predict that the player will work again when the batteries are replaced, we are assuming that there is nothing wrong with the player. But it might turn out that this assumption is wrong. In such situations the falsity of the prediction does not logically entail the falsity of the hypothesis. We might depict the situation by this argument : ( H=The batteries are dead, A=The player is not broken.)

If ( H and A ) then P. It is not the case that P. Therefore, it is not the case that H.

This argument here is of course not valid. When P is false, what follows is not that H is false, only that the conjunction of H and A is false. So there are three possibilities: (a) H is false but A is true, (b) H is true but A is false, or (c) both H and A are false. So we should argue instead:

If ( H and A ) then P. It is not the case that P. Therefore, it is not the case that H and A are both true.

Returning to our earlier example, if the player still does not work when the batteries are replaced, this does not prove conclusively that the original batteries are not dead. This tells us that when we apply the HD method, we need to examine the additional assumptions that are invoked when deriving the predictions. If we are confident that the assumptions are correct, then the falsity of the prediction would be a good reason to reject the hypothesis. On the other hand, if the theory we are testing has been extremely successful, then we need to be extremely cautious before we reject a theory on the basis of a single false prediction. These additional assumptions used in testing a theory are known as "auxiliary hypotheses".

§4. When should we reject a theory?

When a theory makes a false prediction, sometimes it can be difficult to know whether we should reject the theory or whether there is something wrong with the auxiliary hypotheses. For example, astronomers in the 19th century found that Newtonian physics could not fully explain planet Mercury's orbit. It turns out that this is because Newtonian physics is wrong, and you need relativity to give a more accurate prediction of the orbit. However, when astronomers discovered Uranus in 1781, they also found out that its orbit was different from the predictions of Newtonian physics. But then scientists realized that it could be explained if there was an additional planet which affected Uranus, and Neptune was subsequently discovered as a result.

In 2011, scientists in Italy reported that their experiment seemed to have shown that some subatomic particles could travel faster than the speed of light, which would seem to show that relativity theory is wrong. But on closer inspection, it was discovered that there was a problem with the experimental setup. So if a theory has been very successful, even when a result seems to show that theory is wrong, we need to make sure that the evidence is strong and reliable, and try to replicate the result and eliminate alternative explanations. "Extraordinary claim requires extraordinary evidence".

[S03] Theory choice

Whether in scientific research or in everyday life, we often need to choose between alternative explanations or theories. Here are six criteria we can use to evaluate them and help us decide which to accept.

1. Consistency with observations

What are the facts or observations we are trying to explain? Are they incompatible with any of the theories? If so, this will be a good reason to reject them, unless there are reasons to think that some of the observations are not reliable.

2. Predictive power

A scientific theory ought to help us make predictions and explain our observations. If a hypothesis generates no testable prediction, it fails the minimal requirement for a scientific hypothesis.

When we evaluate the predictive power of a theory, we consider both the quantity and the quality of the predictions. How many predictions can the theory make? How accurate and precise are they? Does the theory make predictions across a wide range of phenomena?

3. Mechanism

In general, we want theories that can explain the connections between events by revealing the underlying causal mechanisms. This can help us generate more predictions to test the theory and make other discoveries.

4. Fruitfulness

This is about whether a theory helps us make surprising or unexpected predictions which turn out to be correct, and whether the theory helps us detect and explain connections which we would not have noticed otherwise.

5. Simplicity

A simple theory is (roughly) one with fewer assumptions, and which posits less entities than its competitors. Many scientists believe strongly that we should search for simple theories if feasible.

6. Coherence

A theory should be internally coherent in the sense that it is logically consistent. If not, there is something wrong with the theory as it stands, and so there is a need to revise the theory to come up with a better version.

The other aspect of coherence is that we should look for theories that fit together with other well-confirmed facts and scientific theories. Widely accepted theories are already well-confirmed, so if a hypothesis is incompatible with existing science, the default response should be that the hypothesis is mistaken. An extraordinary claim incompatible with scientific knowledge should require very strong evidence before it can be accepted.

One piece of writing very relevant to the topic under discussion comes from the famous scientist and writer Carl Sagan. In one of his books he proposed what he calls “A Baloney Detection Kit,” a set of tools useful for scientific and everyday reasoning. Here they are:

1. Wherever possible there must be independent confirmation of the “facts.”

2. Encourage substantive debate on the evidence by knowledgeable proponents of all points of view.

3. Arguments from authority carry little weight — “authorities” have made mistakes in the past. They will do so again in the future. Perhaps a better way to say it is that in science there are no authorities; at most, there are experts.

4. Spin more than one hypothesis. If there’s something to be explained, think of all the different ways in which it could be explained. Then think of tests by which you might systematically disprove each of the alternatives.

5. Try not to get overly attached to a hypothesis just because it’s yours. It’s only a way station in the pursuit of knowledge. Ask yourself why you like the idea. Compare it fairly with the alternatives. See if you can find reasons for rejecting it. If you don’t, others will.

6. If whatever it is you’re explaining has some measure, some numerical quantity attached to it, you’ll be much better able to discriminate among competing hypotheses. What is vague and qualitative is open to many explanations.

7. If there’s a chain of argument, every link in the chain must work (including the premise) — not just most of them.

8. Occam’s Razor. This convenient rule-of-thumb urges us when faced with two hypotheses that explain the data equally well to choose the simpler.

9. Always ask whether the hypothesis can be, at least in principle, falsified ... You must be able to check assertions out. Inveterate skeptics must be given the chance to follow your reasoning, to duplicate your experiments and see if they get the same result.

[S04] Causation

In science and everyday life, we are often interested in finding causes and using them to explain and control things. However, the nature of causation is a difficult philosophical topic. Some people think that causation is transfer of energy, others think that causation is a matter of manipulability. There are even philosophers who deny the existence of causation. Here we will discuss some of the useful concepts for thinking about causation in everyday life, and set aside the more controversial issues. (See the section on causation in the Stanford Encyclopedia of Philosophy if you are interested in these issues.)

§1. Some useful terminology

We often talk about causation in two different ways. First of all, there is singular causation, which is a relation between two particular events, where a particular event is some activity or occurrence at some particular time or place. Here are some examples of singular causation :

· Her singing causes the windows to shatter.

· The viral infection caused his death.

But we also speak of general causation as a relation between two types of events, as in :

· Smoking causes cancer.

· Heavy exercises cause sweating, thirst, and fatigue.

It seems reasonable to think that general causation is to be analysed in terms of singular causation. So "type X events cause type Y events" might be understood as something roughly like "particular events of type X are highly likely to cause particular events of type Y."

These concepts about causal connections are also quite useful:

· An event X is causally necessary for an event Y if and only if Y would not have happened if X had not occurred.

· An event X is causally sufficient for an event Y if and only if the presence of X alone is enough to bring about Y.

So for example, heating a gas is causally sufficient but not necessary to increase its pressure - you can increase its pressure by compressing the gas as well. Pressing the light switch might be causally necessary to turn the light on but it is not sufficient since electricity is also required.

· Sometimes, a causal factor can be salient or relevant to the effect even if it is neither necessary nor sufficient, e.g. hardwork might be a causally relevant factor that is part of the explanation of why a student has passed, but presumably it is neither necessary nor sufficient.

· We can also draw a distinction between triggering and standing or structural causes. A triggering cause is a cause that sets into motion the chain of events that lead to an effect. Whereas a standing cause is some static condition that contributes to the effect only in conjunction with a triggering cause.

For example, suppose there was an explosion in a room full of flammable gases. The triggering cause might be the event of someone lighting a match in the room, and the presence of the gases would be the standing cause. Similarly, the standing cause of a particular riot might have to do with high unemployment, with the triggering cause being some particular event such as perhaps someone being beaten up by the police.

§2. Causation and causal mechanisms

The universe contains objects and processes at various levels. Bigger objects such as galaxies are made up of stars and planets, and societies are composed of smaller things such as individual human beings. Similarly, high level processes such as the conduction of electricity is composed of lower-level processes such as the movement of electrons. To explain causation, it is not enough just to know that A is the cause of B, we need a theory that explains how A causes B. What is needed is a theory of the lower-level causal mechanisms that lead from A to B.

For example, to explain why heating causes a piece of metal to expand, we cite the fact that heating gives energy to the metal atoms, and as a result of increasing vibration due to higher energy the distance between the atoms increase and this constitutes expansion. The structure of this explanation can be represented by a diagram :

What this diagram shows is that a high level physical causal process is explained in terms of lower-level mechanisms. Without lower-level mechanisms, we would not be able to understand how high-level causation can occur. This applies not just to physics but to other disciplines as well. For example, in macroeconomics, high-level properties like GDP, inflation and unemployment rate are also to be explained at a lower level by the actions of individuals in the economy.

[S05] Mill's methods

John Stuart Mill (1806-1873) was an English philosopher who wrote on a wide range of topics ranging from language and science to political philosophy. The so-called "Mill's methods" are five rules for investigating causes that he has proposed. It has been suggested that some of these rules were actually discussed by the famous Islamic scientist and philosopher Avicenna (980-1037).

§1. The Method of Agreement

The best way to introduce Mill's methods is perhaps through an example. Suppose your family went out together for a buffet dinner, but when you got home all of you started feeling sick and experienced stomach aches. How do you determine the cause of the illness? Suppose you draw up a table of the food taken by each family member :

Member / Food taken	Oyster	Beef	Salad	Noodles	Fallen ill?
Mum	Yes	Yes	Yes	Yes	Yes
Dad	Yes	No	No	Yes	Yes
Sister	Yes	Yes	No	No	Yes
You	Yes	No	Yes	No	Yes

Mill's rule of agreement says that if in all cases where an effect occurs, there is a single prior factor C that is common to all those cases, then C is the cause of the effect. According to the table in this example, the only thing that all of you have eaten is oyster. So applying the rule of agreement we infer that eating oyster is the cause of the illnesses.

§2. The Method of Difference

Now suppose the table had been different in the following way:

Member / Food taken	Oyster	Beef	Salad	Noodles	Fallen ill?
Mum	Yes	Yes	Yes	Yes	Yes
Dad	Yes	Yes	Yes	Yes	Yes
Sister	Yes	Yes	Yes	Yes	Yes
You	Yes	Yes	No	Yes	No

In this particular case you are the only one who did not fall ill. The only difference between you and the others is that you did not take salad. So that is probably the cause of the others' illnesses. This is an application the method of difference. This rule says that where you have one situation that leads to an effect, and another which does not, and the only difference is the presence of a single factor in the first situation, we can infer this factor as the cause of the effect.

§3. The Joint Method

The joint method is a matter of applying both the method of agreement and the method of difference, as represented by the diagram above. So application of the joint method should tell us that it is the beef which is the cause this time.

Member / Food taken	Oyster	Beef	Salad	Noodles	Fallen ill?
Mum	Yes	Yes	Yes	Yes	Yes
Dad	Yes	Yes	No	Yes	Yes
Sister	Yes	Yes	Yes	No	Yes
You	Yes	No	No	Yes	No

§4. The Method of Concomitant Variation

The method of concomitant variation says that if across a range of situations that lead to a certain effect, we find a certain property of the effect varying with variation in a factor common to those situations, then we can infer that factor as the cause.

Thus using the same kind of example, we might find that you felt somewhat sick having eaten one oyster, whereas your sister felt rather not well having eaten a few, and your father became critically ill having eaten ten in a row. Since the variation in the number of oysters corresponds to variation in the severity of the illness, it would be rational to infer that the illnesses were caused by the oysters.

§5. The Method of Residues

According to the method of residues, if we have a range of factors believed to be the causes of a range of effects, and we have reason to believe that all the factors, except one factor C, are causes for all the effects, except one, then we should infer that C is the cause of the remaining effect.

§6. General comments on Mill's methods

Mill's methods should come as no surprise, as these rules articulate some of the principles we use implicitly in causal reasoning in everyday life. But it is important to note the limitations of these rules.

· First, the rules presuppose that we have a list of candidate causes to consider. But the rules themselves do not tell us how to come up with such a list. In reality this would depend on our knowledge or informed guesses about likely causes of the effects.

· The other assumption presupposed by these methods is that among the list of factors under consideration, only one factor is the unique cause of the effect. But there is no guarantee that this assumption always holds. Also, sometimes the cause might be some complicated combinations of various factors.

How do we infer causation based on our observations? This is not an easy task. A typical starting point is that we notice that one thing follows another, and then we try to determine whether there is a causal connection between the two. Suppose your phone is not working, and you realize the weather is very humid. Does the high humidity cause the phone to malfunction? This might just be a coincidence, but if this happens regularly, then you might be more confident that there is a causal connection. In a lot of situations, this is indeed how we infer causation. An event A is regularly followed by B, and we infer that A is the cause of B.

However, this reliability of this inference depends on our ability to rule out other explanations. When there is a correlation between events A and B, one possibility is that A causes B. But there are other possibilities we ought to consider. In this tutorial, we list some of these possibilities. So remember them next time when you try to identify causes and effects.

1. The correlation between A and B is accidental

· It is probably true that whenever a baby is born, someone somewhere in the world will die on the same day. But this is hardly surprising given the number of people dying and being born each day. Any connection between the two is purely an accident.

· To see whether the connection between A and B is an accident, it is important to consider a control situation where A is absent, and see if B would still occur. This is a very important of scientific thinking. But it applies to everyday life too. Some people think that playing music to plants will make them grow better. But we need to check whether tomatoes growing in similar conditions without the music will grow just as well or not.

· See also the Simpson's paradox.

· In fact, if you look long and hard enough, it is not difficult to come up with some spurious accidental correlations. There is in fact a website with lots of real data that allows you to discover some funny correlations, such as the connection between cheese consumption and the number of people who died by being tangled in their bedsheets:

The diagram above and many other ones can be found here.

2. B is the cause of A

· Sometimes correlation goes both ways. The fact that A causes B can explain the correlation, but maybe the reality is that B is the cause of A. For example, people who are depressed tend to have low self-esteem. Perhaps the former is the cause of the latter, but it is also possible that low self-esteem causes depression by making a person socially withdrawn and lacking in motivation. We need further observations to determine which possibility it is.

3. A and B form a causal loop

· In many cases two causal factors can reinforce each other by forming a causal loop. In the example above, it is more plausible to think that depression affects self-esteem, and a lower self-esteem can cause further depression.

· Of course, causal loops happen only between types of events. If a particular event A is the cause of a particular event B, then A must happen earlier than B and so B cannot be the cause of A.

4. A and B have a common cause

· Young children with larger noses tend to be more intelligent, but it is not because the nose size somehow accelerates cognitive development. Rather young children with larger noses are children who are older, and older children are more intelligent than younger ones because their brains have developed further. So A and B are correlated not because A is the cause of B, but because there is an underlying common cause.

5. A is a minor cause of B

· An effect can have more than one cause, and some may be more important than others.

6. B is a side effect of A

· These are cases where the effect might be wrongly attributed to A when in fact it is due to some side effect of A.

· It has been shown that medicine can have a placebo effect. The subjective belief that one is being treated can bring about relief from an illness even if the medical treatment being given is not really effective against the illness. For example, a patient might report that his pain has decreased as a result to taking a pill, even though the pill is a sugar pill with no effect on pain.

 Exercise #1

Suppose these correlations have been observed. For each of them, try to come up with different possible causal explanations.

1. Shark attacks correlate with ice-cream consumption.

2. A recent study finds that people who use two monitors are 44% more productive than those who are using a single monitor.

3. People who consume more expensive organic food regularly are healthier than those who do not eat organic food.

4. Children who eat breakfast are more likely to do better at school. （See Littlecott, H. J., Moore, G. F., Moore, L., Lyons, R. A., & Murphy, S. (2016). Association between breakfast consumption and educational outcomes in 9–11-year-old children. Public health nutrition, 19(09), 1575-1582.)

 Exercise #2

Here is another interesting chart showing why we should not confuse correlation with causation:

[S07] Causal diagrams

The world being a complicated place, events are often related by complex causal connections. Cause and effect diagrams can play a very important role in understanding such connections, and assist in the calculation of statistical and probabilistic dependencies. By laying out such connections, diagrams can help us identify important crucial factors in the explanation, prediction and control of events. Here we discuss briefly two popular types of cause and effect diagrams.

§1. Causal networks

Causal networks are diagrams that indicate causal connections using arrows. Here is a simple example where an arrow from A to B indicates that A is the cause of B.

Causal networks are particularly useful in showing causal processes which involve a number of different stages. Here is a beautiful diagram created by Alfred Barr, the first director of the MOMA museum, showing the influences between movements in modern art:

In science, the arrows in a cause and effect diagram can be given probability assignments to indicate how likely it is that one event would lead to another. Special algorithms or programs can then be used to calculate how likely it is for a particular effect to come about. These networks with probability are known as "Bayesian networks" or Belief nets".

§2. Fishbone diagrams

Fishbone diagrams are so-called because they resemble fishbones. They are also called "Ishikawa diagrams", named after Kaoru Ishikawa of Japan. A fishbone diagram is a graphical representation of the different factors that contribute to an effect. They are often used in business and management.

In a typical fishbone diagram, the effect is usually a problem to be resolved, and is placed at the "fish head". The causes of the effect are then laid out along the "bones", and classified into different types along the branches. Further causes can be laid out alongside further side branches. So the general structure of a fishbone diagram is something like this:

Here is an example of how a fishbone diagram can be used to display different types of causes:

One advantage of these diagrams is that they give a big picture of the main causal factors leading to the effect. These diagrams are now often used in quality management, and in brainstorming sessions.

[S08] Causal fallacies

Here are some typical mistakes in causal reasoning:

· Post hoc fallacy - Inferring that X causes Y just because X is followed by Y. Example: "Last time I wore these red pants I got hit by a car. It must be because they bring bad luck."

· Mistaking correlation as causation - "Whenever I take this pill my cough clears up within a week, so this pill is very effective in curing coughs." But perhaps mild coughs go away eventually even without taking medicine?

· Reversing causal direction - Assuming that X causes Y without considering the possibility that Y is the cause of X - "Children who like violent video games are more likely to show violent behavior. This must be because they are copying the games." But can it be that children who are more prone to violence are more fond of such video games?

· Genetic fallacy - Thinking that if some item X is associated with a source with a certain property, then X must have the same property as well. But of course this might not be the case. Example: "Eugenics was practiced by the Nazis so it is obviously disgusting and unacceptable."

· Fallacy of the single cause - Wrongly presupposing that an event has a single cause when there are many causally relevant factors involved. This is a fallacy where causal interactions are being over-simplified. For example, after tragedy such as a student committing suicide, people and the news media might start looking for "the cause", and blame it on either the parents, the amount of schoolwork, the society, etc. But there need not be a single cause that led to the suicide. Many factors might be at work.

· Confusing good causal consequences with reasons for belief - Thinking that a claim C must be true because believing in C brings about some benefit. Example: "God exists because after I have become a believer, I am a lot happier and is now a better person."

[S09] Scientific research

It is inevitable and also prudent that we make use of scientific research in our daily life. We might look up information about what to eat to become healthier, or we might want to find out what is the best way of learning a new language.

One thing we should remember is that science is a human construction. It is the product of human beings who do not know everything, and who can be biased. So here are a few things to bear in mind when we read scientific research and make use of such information.

· Scientific theories progress through trial and error. As we learn more, old theories are rejected and improved upon. So we should expect that many things we accept as scientific facts right now might turn out to be wrong later on. Keeping an open mind is thus a good attitude. However, this does not mean it is irrational to accept scientific findings. What we think we know right now might be the approximate truth even if not exactly right. Also, although science might be wrong about many things, there are lots of other things which we can be quite confident about, e.g. the Earth is not flat, water contains oxygen and hydrogen, etc.

· Very often scientific studies are carried out but not published or reported. For example, a pharmaceutical company might do an experiment to test whether a drug is effective, but does not publish the result when it fails to find a positive effect. This is something to bear in mind especially in the field of medicine. One paper reporting a positive effect in a clinical trial might turn out to be an unreliable anomaly if there are many other unpublished results with the opposite conclusion.

· Results that can be replicated are of course more reliable. Recently some experts found that about 75% of social psychology experiments published in top journals cannot be replicated.

· Scientists, like ordinary human beings, can be biased. Some might not be completely objective when it comes to evaluating evidence. Others might be incompetent or careless in running their experiments. There are also those who are downright dishonest and fabricate their results. So it is sensible to be skeptical of findings which have not gone through a rigorous peer review process.

· Extraordinary discoveries that go against established theories in the field should be treated with caution unless they can be replicated by others and evaluated more objectively.

· Science is often expensive. Scientists depend on an adequate source of funding to do their work. So some might be tempted to publish only results that are favorable to the companies that fund their work, and to suppress unfavorable evidence. So it is crucial for scientists to reveal the source of their funding and to avoid conflicts of interests.

· We get a lot of information about scientific research indirectly through various news channels and social media. Very often the people who report such news might not be scientific experts and can easily get things wrong. For example, very often a discovery about a correlation between X and Y will be reported as a causal claim: X causes Y. But the scientists themselves might be more cautious. So if you have time, looking up the actual research publication might give you a more accurate picture. Very often the abstract of the article is available for free on the internet. Try to identify websites which offer more high-quality content. Do not put your trust in everything you read in online forums. Verify for yourself that the information is correct and cross-check with other sources.

· Nobody knows everything about science. A scientist who is an expert in one area might not even understand fully the basic principles of a different area of science. So be cautious of experts who make big claims about some topic that is not in his field.