Experimental Measurement
Experimental Measurement - Related Videos
10:36
Advanced Experimental Methods for Low-temperature Magnetotransport Measurement of Novel Materials
0 Views •
Cited by 2 •
2016
We describe the methodology of mechanical exfoliation and deposition of flakes of novel materials with micron-sized dimensions onto substrate, fabrication of experimental device structures for transport experimentation, and the magnetotransport measurement in a dry helium close-cycle cryostat at temperatures down to 0.300 K and magnetic fields up to 12 T.
07:01
Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children
0 Views •
Cited by 4 •
2019
An experimental paradigm was created to measure the effects of self-distancing in young children (4-6-year-olds). Self-distancing is a process through which individuals adopt a less egocentric perspective. This paradigm has been used to examine the effects of self-distancing on young children's self-regulation.
07:49
Development of an Experimental Setup for the Measurement of the Coefficient of Restitution under Vacuum Conditions
0 Views •
Cited by 11 •
2016
The coefficient of restitution is a parameter that describes the loss of kinetic energy during collision. Here, a free-fall setup under vacuum conditions is developed to be able to determine the coefficient of restitution parameter for particles in micrometer range with high impact velocities.
05:33
Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
0 Views •
2020
Here, we present a protocol to measure the effect of positive induced emotion on grammar learning in foreign language learners using a semi-artificial language that integrates the grammatical rules of a foreign language with the lexicon of the learners' native language.
04:30
An Experimental Paradigm for Measuring the Effects of Ageing on Sentence Processing
0 Views •
2019
Here, we present a protocol to examine the age-related decline in sentence processing using a maze task that allowed us to localize the processing difficulty at each word of the sentences during reading.
FAQs
Calibration establishes how instrument readings correspond to accepted standards before data collection begins. This step helps identify or reduce discrepancies between the displayed value and the physical quantity being assessed. Recalibration or calibration checks can also reveal changes in apparatus performance, making later comparisons with theoretical predictions more reliable and supporting reproducible results.
Systematic errors produce consistent influences that can shift results in one direction, whereas random errors cause variation among repeated observations. Separating these effects helps researchers decide whether to improve the apparatus, control an experimental condition, or use repeated measurements to characterize variability. The distinction is essential when judging whether an observed effect is real or noise.
Uncertainty analysis indicates how measurement limits affect the value reported for a physical quantity. It prevents researchers from presenting more precision than the experiment supports and helps determine whether differences between observations, standards, or predictions are meaningful. Including appropriate uncertainty makes results easier to compare, interpret, and communicate without overstating the evidence.
Reliability depends on controlling relevant conditions, using calibrated equipment, and recording repeated observations consistently. Uncontrolled changes can introduce systematic influences, while variation between trials can increase random error. Comparing the resulting data with standards or theoretical predictions then helps reveal whether the apparatus and procedure produce stable evidence suitable for testing a physical law or model.
A typical workflow begins by calibrating the equipment and defining the measurement procedure. Researchers control relevant conditions, record repeated observations, and examine the resulting data for systematic and random errors. They then estimate uncertainty, compare the measurements with standards or theoretical predictions, and report the value with precision that reflects the experiment's limitations.
Physicists use measured data to evaluate whether a theoretical model or physical law agrees with observation. The comparison can support a model, expose discrepancies, or show that the apparatus cannot resolve a predicted effect because of noise or uncertainty. These results guide improvements to the experiment and can contribute to determining physical constants.
To determine a physical constant, researchers obtain quantitative observations under controlled conditions and compare the results with relevant standards or theoretical predictions. Repeated measurements help reveal random variation, while error analysis identifies systematic influences and uncertainty. The final reported value communicates both the estimate and the limits imposed by the apparatus and procedure.