BM3101

Importance of experimental design: save time& money, address ethical issues, generate useful data, minimise random variation, account for confounding factors
Goals in designing experiments: reduce inter-dependent samples, minimize random variation, account for confounding factors
Independent data points: When the measured value of one individual has no effect on the possible values another individual will have
Sample: subjects in an experiment
Population: wider set of individuals the sample represents
Names for random variation: Noise, inter-individual variation, between-individual variation, within treatment variation
Random variation: the differences between measured values of the same variables taken from different individuals
Confounding factor: an unknown variable that influences the response variable and creates the appearance of a relationship between the studied explanatory& response variable
Hypothesis: a clear statement articulating a plausible explanation for observations& designed so that it can be supported or refuted by the gathering of data
Order of scientific method: Observation → question → hypothesis → prediction
Pilot study: an exploration of the study system, conducted before the main body of data collection, in order to refine research aims and data-collection techniques
Correlative/observational study: examine whether 2 or more processes tend to co-occur/are mutually exclusive without altering the experimental system
Mechanistic/manipulative/intervention studies: provide evidence for interaction between pathways & their involvement in a biological process by altering the experimental system
Advantages/disadvantages of correlative study: Cannot prove causative relationships or process interaction, less work & money, less intervention when animals involved, Manipulations may have unintended consequences & cause generated data to be irrelevant, can suffer from confounding factors and reverse causation
Advantages/disadvantages of mechanistic study: can avoid reverse causation or confounding factors, more work, money, and time, manipulations may have unintended consequences
in Vitro: performing a given procedure in a controlled environment outside of a living
organism
Issue with in vitro models: do not replicate the precise cellular conditions of an organism, so results may not correspond to the circumstances occurring in a living organism
in Vivo: experimentation using a whole, living organism as opposed to a partial or dead organism
in Silico: experimentation carried out on computer or via computer simulation
Features of good experimental design: clear objectives, avoid systematic error, be unbiased, sufficient power, be precise, be reproducible, randomised comparisons, allow error estimation, able to undergo statistical analysis, have broad validity
Systematic error: error that is not determined by chance but is introduced by an inaccuracy inherent in the system
Precision: now close repeated measurements are to each other (measure of random error)
Factors in precision: experimental design, sample size, and size of the random errors
Experimental factors: aspects of experiment that change/influence the experiment's outcome
Continuous: ordered numerical (body weight in kg)
Discrete: unordered numerical (number of cells)
Nominal categorical: unordered phrases (gender)
Ordinal categorical: ordered phrases (tumour grade)
Sources of variance: biological variance (noise), technical noise.
Batch effect: when a subset of the sample behaves differently due to measurement/technique variance
Replication: making the same manipulations& measurements on a number of different experimental units
Biological replicates: Increased sample size, more of the same measurement on different subjects
Technological replicates: same sample size, more of the same measurement on the same subject (mean of technical replicates = 1 biological replicate)
Pseudoreplication: taking the incorrect level of replication by artificially inflating the number of replicates to create spurious results
Randomisation: any individual in the population of interest has the same chance as any other individual in that population of finding itself in each experimental group
Power: probability that a statistical test will reject a false hull hypothesis /detecting an effect if it is really there
Factors in power: effect size, standard deviation, amount of random variation, biological differences, experimental design, sample size, number of replicates, one vs two sided test
Effect size: [mean of experimental group]-(mean of control group]/standard deviation
Type I error: False positive; the results show an effect but really there is no effect (2 = 0. 95)
Type II error: False negative; the results show no effect but really there is an effect (B = 0. 2) (power = 1-B)
Critical value: a point on the test distribution that is compared to the test statistic to determine whether or not to reject the null hypothesis (test statistic > critical value = statistical significance)
Specificity: returning a negative result when it is negative (low false positive, high false negative
Sensitivity: returning a positive result when it is positive (low false negative, high false positive)
Control: a reference against which the results of an experimental manipulation can be measured
Purpose of control: establish a baseline for comparison, & reduce effect of confounding variables
Negative control: no manipulation
Positive control: provides data on the outcome of an alternative procedure
Concurrent control: running the controls at the same time as the treatment group
Historic control: using old data as a control (can introduce confounding factors)
Blind procedure: Scientists have no knowledge of which experimental group each subject belongs to, removes concern of scientist/assessor bias
Double blind procedure: subjects also have no knowledge of which experimental group they belong to, avoids subject bias
Placebo effect: if people believe they are receiving treatment, they show improvements in health
Placebo: a vehicle control designed to appear exactly like the real treatment without the experimental variable
Purpose of placebo: subjects continue to participate in experiment, to control every experimental factor except for the parameter under investigation
Main effect: direct effect of explanatory on response variable
Interaction effect: the effect of the explanatory variable is affected by another independent variable
Blocking: splitting experimental subjects into blocks based on another variable that may interfere with the experimental variable (ex: age, gender) before randomly distributing the individuals in each block in turn
Imprecision: adding error to measurements in an uncorrelated manner (problem with reproducibility)
Inaccuracy/bias: adding error to measurements in a correlated manner (systematic error)
Bias: any deviation from the truth in data collection/analysis/interpretation which can cause false conclusions
Intra-observer variability: Inaccuracy introduced by human error
Observer drift: systematic change In a measurement instrument (ex: human observer) such that the measurement taken is affected by when in a sequence of measurements it was taken
Avoiding intra-observer bias: repeatability study, create objective categorising criteria
Repeatability study: a procedure of measuring random samples several times& comparing their values
High repeatability: same score each time something is measured, suggests low imprecision but tells nothing about bias or inaccuracy
Checking bias/inaccuracy: score samples& check against the known true measurement, recruit more observers
Inter-observer variability: error introduced because different observers score the same sample differently
Avoiding inter-observer variability: clearly define rules& methods of scoring, normalise the data, discussion to find resolution
Observer effect: observing a biological system may change the way it behaves
Floor effect: the majority of measurements taken are at the lowest possible value of the range
Ceiling effect: the majority of measurements taken are at the highest possible value of the range
Accuracy: a test's ability to correctly Identify each sample as positive or negative (number of correct assessments/number of all assessments)
Specificity calculation: TP/TP+FN
Sensitivity calculation: TN/TN+FP