Explanation:
As it is known that there are two types of properties. These are extensive and intensive.
Extensive properties : Properties that depend on the size or amount of system. For example, mass, volume etc.
Intensive properties : Properties that do not depend on the size or amount of system. For example, density, melting point, specific heat capacity etc.
On the basis of these properties water and ethanol are distinguished as follows.
- Density of water is 997 kg/
whereas density of ethanol is 789 kg/. Both these liquids can be separated by intensive properties. - Melting point of water is zero degree celsius whereas melting point of ethanol is -114.1 degree celsius.
- Specific heat capacity of water is 4.184
whereas specific heat capacity of ethanol is 2.46 . - Mass of the given liquids cannot be differentiated because they will keep on changing depending on the quantity required. As mass is an extensive property, therefore, it is difficult to differentiate between the two liquids.
Thus, we can conclude that properties like density, melting point, specific heat capacity can help a chemist distinguish between ethanol and water.
Answer:
is there a way of reducing light pollution at the edges of a forest work?
Observation, in which the scientist observes what is happening, collects information, and studies facts relevant to the problem. In this stage, statistics suggests what can most advantageously be observed and how data might be collected.
Hypothesis, in which the scientist puts forth educated hunches or explanations for observed findings and facts. In this stage, the statistician helps format observations in a form that is comprehensible and understandable.
Prediction, in which the anticipatory deductions based on hypotheses are put forward in testable ways. Statistics can help only a little at this stage of analysis, for predictive insights are often intuitive and creative rather than numerical.
Verification, in which data are collected to test predictions. In judging the extent to which predictions are borne out by observation, we recognize that data and predictions almost never agree exactly, even when theories are correct.
