the scientific method planning and data collection

The Scientific Method

Planning and Data Collection

STEP 1 – Project definition. What is to be investigated, and the resources required STEP 2 – Background research. The literature – who’s done what?

STEP 3 – Definition of hypothesis being tested. Refining what and how data will be collected to test the hypothesis

STEP 4 - Data collection planning. Definition of the method(s) of data collection and any controls

STEP 5 – Methodology planning. The tools and techniques that will be used including safety considerations and potential sources of error and bias in measurementsSTEP 6 – Action planning. Where, when, when by and how the investigation will be conducted. How, what, when, from whom the necessary resources will be assembled. Permissions, risk assessments, appointments, booking confirmations etcSTEP 7 - Conduct and review . Time (contingencies) must be built into the action plan

Planning and Data Collection

Lecture 2: Project Planning and Data Collection

A variable is any characteristic that can take on one of a range of values that vary from individual to individual or group to group. The values are termed data – typically measurements of some kind

Variables may be :

Qualitative (categorical nominal variables)non-numerical, descriptive data, e.g. sex, colour, presence / absence of a feature

Ranked (categorical ordinal variables)on a scale representing an order, but without defined intervale.g. abundance (rare, common, abundant) ; colour (dark, medium, pale)

Quantitative numerical values on a well-defined scaleeither discontinuous, where data can only be a whole numbere.g. number of children in a familyor continuous, where data can have any value (within limits!)e.g. height of children in a family, length of fish, chemical concentration

Types of Data


The dependent variable is the response that is being observed and recordedThe independent variable is the influencing factorunder investigation

Field Studies usually have less control over independent variables and need to record a greater variety of data to account for the influence of theseDependent variables are also more often recorded as qualitative , ranked or discontinuous data, e.g.Distribution of animals / plants, presence / absence of a speciesFrequency or abundance of an event or item e.g. particular behaviour, animal, plantDuration of an event or size of an observation e.g. type of behaviour, height of trees are continuous data


Types of Data

Independent variables are more often continuous datae.g. Quantity of light, temperature, soil pH, humidity, ground inclination, mineral or pollutant concentration, stream flow rate, sediment depth, travel distance, food availability etcObservations often sample part of an area or population and data are used to draw conclusions about the whole area or population

Questionnaires are often used to gather data about the distribution or frequency of a dependent variable and should be: Short – the longer the form, the lower the completion rate

Clearly worded - so there can be no confusion about what is being askedEasy to complete - tick boxes, ring a choiceAnalysable – yes / no, select a statement, choose one answerPurposeful – people must see the point of completing the formInclude an SAE – don’t expect people to pay postage!


Types of Data

In biological investigations, for a given value of an independent variable, repeated measurement of the dependent variable will rarely give exactly the same answer Some variation may be due to instrument or human errors inmeasurement, but most is due to differences between individuals

In living organisms, biological variation may be due to any or all of:

Genetics: the larger the population of individuals from which samples are drawn, the greater the genetic variation between individuals, e.g. all sheep versus all Cheviots versus a Cheviot flock

Environment: e.g. diet, housing, disease status

Sex: differences between males and females

Age: e.g. the ratio of muscle mass to whole body mass between infants, adults and geriatric animals

Variations in Measurement


In a mixed population of plants or animals, biological variation may be greater than (and mask) the variation in the characteristic under investigatione.g. a blood test shows a low plasma calcium level – but by how much must it be lowered before it can be considered abnormal?

Statistical tests can be used to help decide when variation in data is due to a real event and not just due to natural biological variationbut only when sampled data represent the population

Population and SamplesThe concept of a population from which our measurements are a sampleis fundamental to the scientific method


Variations in Measurement

A population includes ALL representatives of a particular group of organisms or values of a measurement, of which a sample is a sub-group

and may be represented by:Individuals e.g. all cattle, all beef cattle, all Herefords, all of a herdMeasurements of a variable e.g. liver weights, hormone or enzyme levels in bloodNumber of items e.g. faecal egg counts, blood cell counts

To represent the population, samples must be randomly selected from within itThere must be an equal chance that any measurement could have come from any individual in the whole population

Population and Samples


The size of the sampling unit and the sample size are NOT the same thing!The sampling unit must be chosen to cover the likely range of values of the variable under investigation e.g.

► a pH test kit only covering a range of pH4 to pH8 would not capture values below pH4 and above pH8, but which may occur

► a quadrat size that showed large variation in the numbers of species recorded between samples would not be representative of the diversity of the habitat it was sampling – a larger quadrat would be needed

► a behaviour study during daylight hours only would not capture any nocturnal activity

The sample size (the total number of samples) must capture the range of variation among the populationi.e. it must be sufficient to demonstrate the central tendency of the values of the variable while accurately reflecting the extremes that values may take

Population and Samples


Descriptive statistics - used to describe and summarise the data set and to help present results clearly in tables and diagrams e.g.

indicators of centrality such as mean and mode

indicators of the scatter or dispersion of the data such as variance or range

Inferential statistics - used to estimate population values from sample data

a parameter, such as the mean, median, mode, standard deviation etc , describes certain features of the distribution of a variable

or

Estimation of population values is usually followed by hypothesis testing, which investigates a particular theory about the data

Hypothesis tests allow conclusions to be drawn about the population from the information in a sample

the scientific method planning and data collection

Documents