Manual Practical Physics

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49 records List of physics practicals and experiments with detailed instructions, safety advice and background information.
Table of contents

You may need to suggest improvements. For example, a small heavy object will be less affected by air resistance in this experiment. A small metal ball or a marble would be more suitable than a scrunched up piece of paper or a ping-pong ball.

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While, in contrast, dropping a feather would make the experiment invalid. The table below assesses the validity, reliability and accuracy of the experiment. It also has suggestions for improvements. Increase the starting height from 0. Use data logger and sensor to take more accurate measurements of time.

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Use a tape measure rather than a wooden ruler. You can learn more about validity, reliability and accuracy in Physics Practical Skills Part 2. Learn how to:.


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Students are provided with equipment and possibly instructions and must carry out the experiment. An experiment is valid if the methods are carried out appropriately and the control variables are kept constant. An experiment is reliable if you get very similar results for every experiment.

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An experiment is very accurate if there is small difference between the experimental results and the accepted true value. Suggest experimental techniques that will reduce random errors and improve the reliability of the the experiment.

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Suggest experimental techniques that will reduce systematic errors and improve the accuracy of the the experiment. Conduct a practical investigation to validate the relationship between the variables: initial velocity launch angle maximum height time of flight final velocity launch height horizontal range of the projectile. Conduct investigations to explain and evaluate, for objects executing uniform circular motion, the relationships that exist between: centripetal force mass speed radius.

Investigate the relationship between the centripetal force and period using a centripetal mass balance. Increase the height by 50 cm increments for five different height and record the time taken to fall from each height five times. The experiment is valid as the methods were carried out appropriately and the control variables such as the shape and mass of the ball was are kept constant.

This student has collected six sets of data over an appropriate range of values 2.

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An appropriate non-linear graph has been drawn 3 and the student has correctly stated the type of relationship 4. The student has used increased accuracy by repeating and averaging the time measurements 2 and has reduced parallax error when measuring distances 6.

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The student has made an attempt to identify a variable that needs to be controlled 1 and to construct the correct mathematical equation 5. This student has collected six sets of data over an appropriate range of values 1. An attempt has been made to process the data so that an appropriate non-linear graph can be drawn 2. The student has correctly stated the type of relationship 3. The student has collected data over an appropriate range of values 1 and has attempted to draw an appropriate graph to show the relationship 2.

Print this page. Systematic errors often arise because the experimental arrangement is different from that assumed in Ihe theory, and the correelion facior which takes account of this difference is ignored. It is easy 10 give examples of effects that may lead 10 systematic error. Another common source of systematic error is the one mentioned earlier - inaccurate apparatus. Random errors may be detected by repeating the measurements. Furthermore, by taking more and more readings we obtain from the arithmetic mean a value which approaches more and more closely to the true value.

Neither of these points is true for a systematic error. Repeated measurements with the same apparatus neither reveal nor do they c:liminate a systematic error. For this reason systematic errors are potentially more dangerous than random errors. If large random errors are present in an experiment, they will manifest themselves in a large value of the final quoted error. Thus everyone is aware of the imprecision of the result. However, the concealed presence of a systematic error may lead to an apparently rc:liable result, given with a small estimated error.

A classic example of this was provided by Millikan's oil. In this experiment it is necessary to know the viscosity of air. The value used by Millikan was too low, and as a result the value he obtained for e was. This may be compared with the present value Mohr and Taylor Up till 19 0, the values of several other atomic constants, such as the Planck constant and the Avogadro constant.

Random errors may be estimated by statistical methods, which are discussed in the next two chapters. Systematic errors do not lend themsc:lves to any clear-cut treatment. Your safest course is to regard them as effects to be discove'red and eliminated. There is no general rule for doing this. We shall try to point out common sources of systematic error in this book, but in this matter there is no substitute for experience. Suppose we make a. The individual values Xt. Of course we do nol know the actual error in x. If we did, we would correct i by the required amount and gel the right value X.

The most we can do is to say that there is a certain probability that X lies within a certain range cenlred on x. The problem then is to calculate this range for some specified probability. Results of successive measurements of the same quantity. The mean x is expected to be closer to the true value for set a than for set b. A clue to how we should proceed is provided by the results shown in Fig.

On the whole. In other words. The whole of the present chapter is concerned with putting this qualitative statement on a firm quantitative basis. We assume throughout that no systematic errors are present. Denote the values of n successive measurements of the same quantity by. XI, X2,.

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The number n is not necessarily large and in a typical experiment might be in the range 5 to The mean is. To fix our ideas let us consider a specific experiment in which the resistance of a coil is measured on a bridge. The results arc: listed in Table 3. The mean of these values is 4.

We require a q u a n t i t y that gives a measure of the spread in the 8 values, from which we shall estimate the error in the mean. To define such a quantity we need to introduce: the idea of a distribution - one of the basK: concepts in the theory of statistics. Although we have only n actual measurements, we imagine that we go on making the measurements so that we end up with a very large number N.

We may suppose N is say Since: we are not actually making the measurements, expense is no object.