Chapter 1: Introduction
1.1 Three ‘principles’
1. Locard’s ‘Principle’: A perpetrator will either leave marks or traces on the crime scene, or
carry traces from the crime scene. This is often misquoted as ‘every contact leaves a trace’,
but Locard never actually claimed this.
2. ‘Principle’ of individuality: Two objects may be indistinguishable but no two objects are
identical.
3. Individualism ‘Principle’: If enough similarities are seen between two objects to exclude the
possibility of coincidence, then those objects must have come from the same source.
1.2 Dreyfus, Bertillon, and Poincaré
Dreyfus was charged with treason and part of the evidence was the comparison of the handwriting
in an incriminating note with Dreyfus’s own handwriting. A prominent witness for the prosecution
was Alphonse Bertillon. He was well known for proposing a system of anthropometry, which became
known as Bertillonage. Anthropometry simply means the measurement of humans. Bertillonage
required taking a photograph and recording a series of measurements of bone features which were
known not to change after adolescence. The purpose of the system was to determine whether or not
a person had the same measurements as a person who had earlier been arrested. Bertillonage could
not help investigators by providing evidence that a particular person had been at the scene of the
crime.
Later, fingerprints became a far quicker and simpler method of identification than anthropometry.
Not only were fingerprints far simpler and cheaper to obtain and record, but they could also help
investigators identify the perpetrators of crimes.
Poincaré identified a crucial point for forensic science and all reasoning behind evidence in court.
Courts are not concerned with the probability that some observation would be made. They are
concerned with what can be inferred from the fact that the observation has been made. The
question for the court then is what inferences can be drawn as to the guilt of the accused. Poincaré
went on to make the point that single items of evidence enable us to alter our assessment of the
probability of an event but they cannot determine the probability of an event on their own:
“To be able to calculate, from an observed event, the probability of a cause, we need several
data:
1. We need to know what was à priori, before the event, the probability of this cause.
2. We then need to know for each possible cause, the probability of the observed event.
1.3 Requirements for Forensic Scientific Evidence
Photographs as evidence have a number of advantages: they can be transmitted and reproduced
easily and can enable people to be recognised at a distance.
Features of an ideal scientific system for identifying people:
- It uses features that are highly variable between individuals;
- Those features do not change or change little over time;
- Those features are unambiguous so that two experts would describe the same feature the
same way;
- Those features can be transferred to traces at a crime scene; and
, - That it is reasonably simple and cheap to operate.
Few systems will satisfy all these requirements and in particular there may be a trade-off between
the last requirement and the others.
Chapter 2: Interpreting Scientific Evidence
2.1 Relevance and Probative Value
The first requirement of any piece of evidence tendered in court is that it must be relevant. In order
to be considered, an item of evidence must be one that might rationally affect the decision.
2.1.1 Ideal and Useless Evidence
An ideal piece of evidence would be something that always occurs when what we are trying to prove
is true and never occurs otherwise. In real life, evidence this good is almost impossible to find. At the
other end of the scale, some observations are certainly useless as evidence.
2.1.2 Typical Evidence
Ideal evidence is seldom found. Even if the evidence always occurs when the hypothesis is true, it
may also occur when it is not. Alternatively, when the hypothesis is true, the evidence may not
invariably occur. Thus, in the real world, evidence is something that is more or less likely to occur
when what we are trying to prove is true, than when it is not.
A scientific test result is good evidence for a particular hypothesis if it is much more likely to occur if
the hypothesis is true, than if it is false. We will know this only if we have seen the result of the test
both on a number of occasions when the hypothesis is true, but also when its negation is true. Even
when we have evaluated the probability of the result under both hypotheses, we still only know the
strength of the evidence in favour of a hypothesis and not the probability that the hypothesis is true.
2.1.3 An Aside on Probability and Odds
Probability is a rational measure of one’s degree of belief in the truth of a proposition based on
information. The hypothesis, proposition, or premise is itself either true or false. All probabilities
depend on the assumptions and information used in assigning them. There are no ‘real probabilities’
that we are attempting to estimate. All the information that is used to assign a probability is known
as the condition for the probability. All probabilities are conditional on the evidence used and
background knowledge.
Probabilities take values between 0 and 1. A probability of 0 means that (taking into account the
evidence listed in the condition) the proposition cannot be true, and we are completely convinced it
is false. A probability of 1 means that, given the condition, the proposition must be true. Most
probabilities fall between these limits. A probability of 0.5 for a proposition means that we are
equally sure (or equally unsure) that the proposition is true and that its negation is true. Probabilities
can be expressed as a percentage (0.5 50%).
We can also express probabilities in the form of odds. To get the odds from the probability of a
proposition, you calculate the ratio of its probability to the probability of its negation and simplify as
much as possible. Thus, a probability of 0.3 has equivalent odds of:
probability 0.3 0.3 3
odds= = = =
1− probability 1−0.3 0.7 7
1.1 Three ‘principles’
1. Locard’s ‘Principle’: A perpetrator will either leave marks or traces on the crime scene, or
carry traces from the crime scene. This is often misquoted as ‘every contact leaves a trace’,
but Locard never actually claimed this.
2. ‘Principle’ of individuality: Two objects may be indistinguishable but no two objects are
identical.
3. Individualism ‘Principle’: If enough similarities are seen between two objects to exclude the
possibility of coincidence, then those objects must have come from the same source.
1.2 Dreyfus, Bertillon, and Poincaré
Dreyfus was charged with treason and part of the evidence was the comparison of the handwriting
in an incriminating note with Dreyfus’s own handwriting. A prominent witness for the prosecution
was Alphonse Bertillon. He was well known for proposing a system of anthropometry, which became
known as Bertillonage. Anthropometry simply means the measurement of humans. Bertillonage
required taking a photograph and recording a series of measurements of bone features which were
known not to change after adolescence. The purpose of the system was to determine whether or not
a person had the same measurements as a person who had earlier been arrested. Bertillonage could
not help investigators by providing evidence that a particular person had been at the scene of the
crime.
Later, fingerprints became a far quicker and simpler method of identification than anthropometry.
Not only were fingerprints far simpler and cheaper to obtain and record, but they could also help
investigators identify the perpetrators of crimes.
Poincaré identified a crucial point for forensic science and all reasoning behind evidence in court.
Courts are not concerned with the probability that some observation would be made. They are
concerned with what can be inferred from the fact that the observation has been made. The
question for the court then is what inferences can be drawn as to the guilt of the accused. Poincaré
went on to make the point that single items of evidence enable us to alter our assessment of the
probability of an event but they cannot determine the probability of an event on their own:
“To be able to calculate, from an observed event, the probability of a cause, we need several
data:
1. We need to know what was à priori, before the event, the probability of this cause.
2. We then need to know for each possible cause, the probability of the observed event.
1.3 Requirements for Forensic Scientific Evidence
Photographs as evidence have a number of advantages: they can be transmitted and reproduced
easily and can enable people to be recognised at a distance.
Features of an ideal scientific system for identifying people:
- It uses features that are highly variable between individuals;
- Those features do not change or change little over time;
- Those features are unambiguous so that two experts would describe the same feature the
same way;
- Those features can be transferred to traces at a crime scene; and
, - That it is reasonably simple and cheap to operate.
Few systems will satisfy all these requirements and in particular there may be a trade-off between
the last requirement and the others.
Chapter 2: Interpreting Scientific Evidence
2.1 Relevance and Probative Value
The first requirement of any piece of evidence tendered in court is that it must be relevant. In order
to be considered, an item of evidence must be one that might rationally affect the decision.
2.1.1 Ideal and Useless Evidence
An ideal piece of evidence would be something that always occurs when what we are trying to prove
is true and never occurs otherwise. In real life, evidence this good is almost impossible to find. At the
other end of the scale, some observations are certainly useless as evidence.
2.1.2 Typical Evidence
Ideal evidence is seldom found. Even if the evidence always occurs when the hypothesis is true, it
may also occur when it is not. Alternatively, when the hypothesis is true, the evidence may not
invariably occur. Thus, in the real world, evidence is something that is more or less likely to occur
when what we are trying to prove is true, than when it is not.
A scientific test result is good evidence for a particular hypothesis if it is much more likely to occur if
the hypothesis is true, than if it is false. We will know this only if we have seen the result of the test
both on a number of occasions when the hypothesis is true, but also when its negation is true. Even
when we have evaluated the probability of the result under both hypotheses, we still only know the
strength of the evidence in favour of a hypothesis and not the probability that the hypothesis is true.
2.1.3 An Aside on Probability and Odds
Probability is a rational measure of one’s degree of belief in the truth of a proposition based on
information. The hypothesis, proposition, or premise is itself either true or false. All probabilities
depend on the assumptions and information used in assigning them. There are no ‘real probabilities’
that we are attempting to estimate. All the information that is used to assign a probability is known
as the condition for the probability. All probabilities are conditional on the evidence used and
background knowledge.
Probabilities take values between 0 and 1. A probability of 0 means that (taking into account the
evidence listed in the condition) the proposition cannot be true, and we are completely convinced it
is false. A probability of 1 means that, given the condition, the proposition must be true. Most
probabilities fall between these limits. A probability of 0.5 for a proposition means that we are
equally sure (or equally unsure) that the proposition is true and that its negation is true. Probabilities
can be expressed as a percentage (0.5 50%).
We can also express probabilities in the form of odds. To get the odds from the probability of a
proposition, you calculate the ratio of its probability to the probability of its negation and simplify as
much as possible. Thus, a probability of 0.3 has equivalent odds of:
probability 0.3 0.3 3
odds= = = =
1− probability 1−0.3 0.7 7
