week 5 exp designs Flashcards
noise
random variation that disrupts relevant information
what 3 types of noise are there
stimulus precision eg auditory stimulus
participant noise eg motivation
precision of data recording eg buffer of exact time
signal
core element that a person is trying to focus on or interpret.
what does noise decrease
reliability of measurement - more noise = less likely to get same results across measurements
aim of experimental control
maximize the signal relative to noise ratio
2 ways to deal with noise
minimise
neutralise
how to minimize noise
use instruments with high accuracy and precision eg high-end hardware
maximise usability and minimize errors eg make the task easy to understand - minimize ambiguity
maximise ptsp alertness eg take breaks
how to neutralize noise
uniform random variation -When random variation is uniformly distributed, over time or across multiple observations, the effects of noise can average out. This means that the random fluctuations (noise) become less disruptive to the overall pattern or signal, as the randomness tends to balance out. This allows the signal to emerge more clearly, as noise doesn’t systematically bias the outcome in a particular direction.
LARGER DATASET/SAMPLE = LESS NOISE
noise is not absent but neutralized by calculating…
central tendency
how does higher signal to noise ratio affect type 1 and 2 errors
higher signal to noise ratio reduces false negatives (type 2 error, specificity) and false positives (type 1 error, sensitivity) which increases sensitivity and specificity of results
type 2 error or false negative (beta)
when a researcher or experimenter incorrectly concludes that there is no significant effect or relationship between variables, despite the fact that one is present.
4 ways to control confounders
- fix - keep constant
- randomise
- balancing
- measure and model confound
how to fix/keep constant the confounder
make the potential confounder a control variable eg lab exp
limitations of fixing/keeping confounder constant
fixing is not always possible - order effects
benefit of randomize (neutralize) a confounder
converts confounder (systematic variation) into noise (uniform random variation) - becomes unrelated to IV
any random variation can be neutralized by calculating central tendency now
limitations of using balancing to reduce confounder
only works if you know confounder beforehand
disadvantage of using randomization to reduce confounder
must have sufficient sample size
difference between noise and a confounder
Confounder: A variable that causes a distortion in the relationship between the independent and dependent variables, often leading to incorrect conclusions about causality.
Noise: Random, unsystematic variations or errors in the data that obscure the signal or relationship without creating any false causal associations
how to balance confounder variable
dividing set into 2 or more subsets with roughly the same characteristics
equalize central tendency across conditions so confounder on DV is neutralized
values of the confounder are distributed in equal frequencies across conditions so central tendency is unbiased
what does measure and model mean in terms of reducing confounder
when you can’t fix or neutralize a potential bias, include It when analyzing data
confounder can become an IV - results indicate potential biases
bias
unwanted signal producing spurious results
2 types of bias
independent and dependent bias
independent bias
= systematic errors
systematic tendencies in measurements that are incorrect, but limited to one variable
bias does not vary with the independent variable so can’t be mistaken for causal
dependent bias
= confounder
produces bias in DV, that varies with IV. so interferes with results as produces unwanted, spurious results
