Measurement and Estimation

Today we practiced estimating and measuring length, volume, and weight.  We considered the length of a piece of rope, the volume of a bowl, and the weight of a bag of glass beads. We first estimated blindly, using our own knowledge of centimeters, milliliters, and grams. Then we estimated again, but this time using a reference object for comparison: a string of known length, an 8-ounce juice cup, and a reference weight.

Finally, we used a ruler, a graduated beaker, and a spring scale to measure the objects. For each round we graphed the data obtained by the whole class to see which set of data was the most precise and accurate. To no one’s surprise, the measured data was the most accurate, but the calibrated estimates were pretty accurate as well, once we averaged the whole class’s data. It was interesting to see that having lots of measurements actually increases the overall accuracy of the average measurement! We also talked about how the precision – or repeatability – of a measurement depends mainly on the tool used to measure something, but of course the scientist doing the measuring has to be careful, or he or she may end up with inaccurate and imprecise data!


Estimación & Medición

Hoy practicamos la estimación y medimos longitud, peso y volumen. Medimos el largo de una cuerda, el volumen de un tazón y el peso de una bolsa con mostacillas de vidrio. Primero estimamos de manera ciega solo usando nuestra experiencia, en centímetros, milímetro y gramos. Luego volvimos a estimar, pero esta vez usando un objeto de referencia para poder comparar: un pedazo de cuerda de tamaño conocido, una taza de 8 onzas de jugo y una peso de referencia.

Finalmente, usamos una regla, un vaso precipitado y una balanza para realizar las mediciones de cada objeto. Para cada una de las estimaciones y mediciones graficamos los datos obtenidos por toda la clase, y así pudimos observar cual set de datos fue el más preciso y acertado. Obviamente, los datos de las mediciones fueron los más acertados, pero los datos estimados usando las referencias estuvieron bastante cercanos a la realidad (una vez que promediamos los datos de toda la clase).  Fue muy interesante observar que entre más datos se tiene, más precisos son las mediciones promedio. También conversamos sobre cómo la precisión (o reproducibilidad) de una medición depende básicamente de la herramienta con que se mide; pero que también es muy importante que el científico que realiza la medición lo haga con mucho cuidado, porque puede obtener datos inexactos e imprecisos.


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Phillip has a BS and MS in Biology from Western Washington University, and is currently earning a PhD in Environmental Studies at Antioch University New England. His research interests are in tropical rainforest ecology and animal-plant interactions. He will be doing his doctoral dissertation research on tropical seed dispersal ecology in the montane rainforests of Rwanda. Phillip has taught though various adjunct positions at several colleges in New England, teaching biology, ecology, earth science, environmental science, and general science at Babson College, North Shore Community College, Wheelock College, Merrimack College, and Mount Ida College.

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