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</div><h2>SL Paper 1</h2><div class="question">
<p>Which value of <em>q</em>, in J, has the correct number of significant figures?</p>
<p style="text-align: center;"><em>q </em>= <em>mc</em>Δ<em>T</em></p>
<p>where <em>m </em>= 2.500 g, <em>c </em>= 4.18 J g<sup>−1</sup> K<sup>−1</sup> and Δ<em>T </em>= 0.60 K.</p>
<p>A. 6</p>
<p>B. 6.3</p>
<p>C. 6.27</p>
<p>D. 6.270</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<p>Which molecule has an index of hydrogen deficiency (IHD) = 1?</p>
A. C<sub>6</sub>H<sub>6</sub><br>B. C<sub>2</sub>Cl<sub>2</sub><br>C. C<sub>4</sub>H<sub>9</sub>N<br>D. C<sub>2</sub>H<sub>6</sub>O</div>
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</div>
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</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What is the ratio of the areas of the signals in the <sup>1</sup>H NMR spectrum of pentan-3-ol?</p>
<p>A. 6:4:1:1</p>
<p>B. 6:2:2:2</p>
<p>C. 5:5:1:1</p>
<p>D. 3:3:2:2:1:1</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>A</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>Which feature of a molecule does infrared spectrometry detect?</p>
<p>A. molecular mass</p>
<p>B. bonds present</p>
<p>C. total number of protons</p>
<p>D. total number of proton environments</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What is the index of hydrogen deficiency (IHD) for this molecule? </p>
<p style="text-align: center;"><img src="data:image/png;base64,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" alt></p>
<p>A. 3</p>
<p>B. 4</p>
<p>C. 5</p>
<p>D. 6 </p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>The enthalpy of combustion of ethanol is determined by heating a known mass of tap water in a glass beaker with a flame of burning ethanol.</p>
<p>Which will lead to the greatest error in the final result?</p>
<p>A. Assuming the density of tap water is 1.0 g cm<sup>−3</sup></p>
<p>B. Assuming all the energy from the combustion will heat the water</p>
<p>C. Assuming the specific heat capacity of the tap water is 4.18 J g<sup>−1</sup> K<sup>−1</sup></p>
<p>D. Assuming the specific heat capacity of the beaker is negligible</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What can be deduced from the following <sup>1</sup>H\(\,\)NMR spectrum?</p>
<p style="text-align: center;"><img src="data:image/png;base64,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"></p>
<p>A. There is only one hydrogen atom in the molecule.</p>
<p>B. There is only one hydrogen environment in the molecule.</p>
<p>C. The molecule is a hydrocarbon.</p>
<p>D. There is only one isotope in the element.</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What is the Index of Hydrogen Deficiency (IHD) for 1,3,5-hexatriene (C<sub>6</sub>H<sub>8</sub>)?</p>
<p>A. 1</p>
<p>B. 3</p>
<p>C. 5</p>
<p>D. 6</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What can be determined about a molecule from the number of signals in its <sup>1</sup>H\(\,\)NMR spectrum?</p>
<p>A. Bonds present</p>
<p>B. Molecular formula</p>
<p>C. Molecular mass</p>
<p>D. Number of hydrogen environments</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>Which information can be gained from an infrared (IR) spectrum?</p>
<p>A. Ionization energy of the most abundant element</p>
<p>B. Number of different elements in the compound</p>
<p>C. Bonds present in a molecule</p>
<p>D. Molecular formula of the compound</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">The graph below represents the relationship between two variables in a fixed amount of gas.</p>
<p class="p1" style="text-align: center;"><img src="images/Schermafbeelding_2016-09-27_om_06.25.35.png" alt="N10/4/CHEMI/SPM/ENG/TZ0/04_1"></p>
<p class="p1">Which variables could be represented by each axis?</p>
<p class="p1"><img src="images/Schermafbeelding_2016-09-27_om_06.26.22.png" alt="N10/4/CHEMI/SPM/ENG/TZ0/04_2"></p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">Which are likely to be reduced when an experiment is repeated a number of times?</p>
<p class="p1">A. Random errors</p>
<p class="p1">B. Systematic errors</p>
<p class="p1">C. Both random and systematic errors</p>
<p class="p1">D. Neither random nor systematic errors</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>A</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
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<div class="column">
<div class="page" title="Page 10">
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<div class="page" title="Page 11">
<div class="layoutArea">
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<div class="page" title="Page 11">
<div class="layoutArea">
<div class="column">
<p>What is the relationship between the two variables sketched on the graph?</p>
<p><img style="display: block; margin-left: auto; margin-right: auto;" src="data:image/png;base64,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" alt></p>
<p style="display: inline !important;">A. <em>y</em> is proportional to <em>x</em></p>
<div class="column">B. <em> y</em> is inversely proportional to <em>x</em><br>C. <em>y</em> is proportional to <em>−x</em><br>D. <em>y</em> decreases exponentially with an increase in <em>x</em></div>
</div>
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</div>
</div>
</div>
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</div>
</div>
</div>
</div>
</div>
</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<div class="page" title="Page 10">
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<div class="page" title="Page 10">
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<div class="page" title="Page 11">
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<div class="column">
<p>A measuring cylinder was used to obtain a known volume of a liquid. The volume was read from the top of the meniscus and the liquid completely emptied into a flask. The exact same process was then repeated. Which statement is correct about the overall described procedure and the volumes measured?</p>
<div class="page" title="Page 11">
<div class="layoutArea">
<div class="column">A. There is a systematic error and the volumes measured are accurate.<br>B. There is a random error and the volumes measured are accurate.<br>C. There is a random error and the volumes measured are inaccurate.<br>D. There is a systematic error and the volumes measured are inaccurate.</div>
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</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">A student recorded the volume of a gas as \({\text{0.01450 d}}{{\text{m}}^{\text{3}}}\). How many significant figures are there in this value?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>3</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>4</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>5</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>6</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p class="p1">In spite of the fact that there was a G2 form comment that the question was too easy, 26% of the candidates managed to give an incorrect response and the discrimination index of 0.27 showed it to be a reasonable discriminator.</p>
</div>
<br><hr><br><div class="question">
<p class="p1">The relationship between the pressure, \(P\), and the volume, \(V\), of a fixed amount of gas at a constant temperature is investigated experimentally. Which statements are correct?</p>
<p class="p1">I. <span class="Apple-converted-space"> </span>A graph of \(V\) against \(P\) will be a curve (non-linear).</p>
<p class="p1">II. <span class="Apple-converted-space"> </span>A graph of \(V\) against \(\frac{1}{P}\) will be linear.</p>
<p class="p1">III. <span class="Apple-converted-space"> </span>\(V = {\text{constant}} \times \frac{{\text{1}}}{P}\)</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>I and II only</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>I and III only</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>II and III only</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>I, II and III</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p class="p1">One respondent stated that this question was not suitable for Topic 11. It is true that this MCQ on Topic 10 often does in fact test the AS‘s associated with Topics 11.1 and 11.2. However, Topic 11.3 on Graphical Techniques also is an integral part of Topic 11 and this question in fact links Topic 11.3 explicitly with the pressure-volume relationship from Topic 1.4, so is a completely suitable question for assessing this Topic 11.3, as candidates are required to interpret graphical behaviour. The question was found to be quite challenging for candidates with 40.98% of candidates getting the correct answer D.</p>
</div>
<br><hr><br><div class="question">
<p class="p1">A burette reading is recorded as \(27.70 \pm 0.05{\text{ c}}{{\text{m}}^{\text{3}}}\). Which of the following could be the actual value?</p>
<p class="p1">I. <span class="Apple-converted-space"> </span>\({\text{27.68 c}}{{\text{m}}^{\text{3}}}\)</p>
<p class="p1">II. <span class="Apple-converted-space"> </span>\({\text{27.78 c}}{{\text{m}}^{\text{3}}}\)</p>
<p class="p1">III. <span class="Apple-converted-space"> </span>\({\text{27.74 c}}{{\text{m}}^{\text{3}}}\)</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>I and II only</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>I and III only</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>II and III only</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>I, II and III</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p class="p1">Two respondents stated that the wording of this question was vague. However, this in fact was the easiest question on the entire paper for candidates with 91.01% of candidates getting the correct answer, B.</p>
</div>
<br><hr><br><div class="question">
<p>What is the density, in g\(\,\)cm<sup>−3</sup>, of a 34.79 g sample with a volume of 12.5 cm<sup>3</sup>?</p>
<p>A. 0.359</p>
<p>B. 0.36</p>
<p>C. 2.783</p>
<p>D. 2.78</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
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<p>Which feature of a molecule can be determined from its <sup>1</sup>H NMR spectrum?</p>
A. Number of hydrogen environments<br>B. Total mass of hydrogen atoms present<br>C. Vibration frequency of C–H bonds<br>D. Ionization energy of a hydrogen atom</div>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>A</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p>What is always correct about the molecular ion, M<sup>+</sup>, in a mass spectrum of a compound?</p>
<p>A. The M<sup>+</sup> ion peak has the smallest <em>m/z </em>ratio in the mass spectrum.</p>
<p>B. The <em>m/z </em>ratio of the M<sup>+</sup> ion peak gives the relative molecular mass of the molecule.</p>
<p>C. The M<sup>+</sup> ion is the most stable fragment formed during electron bombardment.</p>
<p>D. The M<sup>+</sup> ion peak has the greatest intensity in the mass spectrum. </p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p class="p1">How many significant figures are there in 0.00370?</p>
<p class="p1">A. 2</p>
<p class="p1">B. 3</p>
<p class="p1">C. 5</p>
<p class="p1">D. 6</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p class="p1">A piece of metallic aluminium with a mass of 10.044 g was found to have a volume of \({\text{3.70 c}}{{\text{m}}^{\text{3}}}\). A student carried out the following calculation to determine the density.</p>
<p class="p1">\[{\text{Density (g}}\,{\text{c}}{{\text{m}}^{ - 3}}{\text{)}} = \frac{{10.044}}{{3.70}}\]</p>
<p class="p1">What is the best value the student could report for the density of aluminium?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>\({\text{2.715 g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>\({\text{2.7 g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>\({\text{2.71 g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>\({\text{2.7146 g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p class="p1">One respondent stated that the question would have been clearer if “most appropriate” was used instead of “best value”. 58.77% of candidates got the correct answer.</p>
</div>
<br><hr><br><div class="question">
<p class="p1">Which would be the best method to decrease the <strong>random </strong>uncertainty of a measurement in an acid-base titration?</p>
<p class="p1">A. Repeat the titration</p>
<p class="p1">B. Ensure your eye is at the same height as the meniscus when reading from the burette</p>
<p class="p1">C. Use a different burette</p>
<p class="p1">D. Use a different indicator for the titration</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>A</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">A student weighs a standard 70.00 g mass five times using the same balance. Each time she obtains a reading of 71.20 g. Which statement is correct about the precision and accuracy of the measurements?</p>
<p class="p1">A. Precise and accurate</p>
<p class="p1">B. Precise but inaccurate</p>
<p class="p1">C. Accurate but not precise</p>
<p class="p1">D. Neither accurate nor precise</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p class="p1">B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p class="p1">Ultraviolet radiation has a shorter wavelength than infrared radiation. How does the frequency and energy of ultraviolet radiation compare with infrared radiation?</p>
<p class="p1"><img src="images/Schermafbeelding_2016-08-08_om_10.13.19.png" alt="M15/4/CHEMI/SPM/ENG/TZ1/06"></p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p class="p1">A</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p>What is the index of hydrogen deficiency, IHD, of 3-methylcyclohexene?</p>
<p style="text-align: center;"><img src="images/Schermafbeelding_2018-08-09_om_13.09.54.png" alt="M18/4/CHEMI/SPM/ENG/TZ1/29"></p>
<p>A. 0</p>
<p>B. 1</p>
<p>C. 2</p>
<p>D. 3</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p>How are the uncertainties of two quantities combined when the quantities are multiplied together?</p>
<p>A. Uncertainties are added.</p>
<p>B. % uncertainties are multiplied.</p>
<p>C. Uncertainties are multiplied.</p>
<p>D. % uncertainties are added.</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What information is provided by <sup>1</sup>H NMR, MS and IR for an organic compound?</p>
<p style="padding-left: 90px;">I. <sup>1</sup>H NMR: chemical environment(s) of protons<br>II. MS: fragmentation pattern<br>III. IR: types of functional group</p>
<p>A. I and II only</p>
<p>B. I and III only</p>
<p>C. II and III only</p>
<p>D. I, II and III</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p class="p1">Which would be the best method to decrease the random uncertainty of a measurement in an acid–base titration?</p>
<p class="p2">A. Ensure your eye is at the same height as the meniscus when reading the burette.</p>
<p class="p2">B. Use a different indicator for the titration.</p>
<p class="p2">C. Use a different burette.</p>
<p class="p2">D. Repeat the titration.</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p class="p1">D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p class="p1">One respondent stated in the G2 form that the answer could be A. In the teacher’s notes of assessment statement 11.1.3 it is stated that “random uncertainties are reduced by repeating readings”. 75.05% of the candidates chose the correct answer D with only 16.05% opting for A. This proved to be the sixth easiest question of the paper with a discrimination index of 0.20.</p>
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<br><hr><br><div class="question">
<p>In an experiment to determine a specific quantity, a student calculated that her experimental uncertainty was 0.9% and her experimental error was 3.5%. Which statement is correct?</p>
<p>A. Only random uncertainties are present in this experiment.</p>
<p>B. Both random uncertainties and systematic errors are present in this experiment.</p>
<p>C. Repeats of this experiment would reduce the systematic errors.</p>
<p>D. Repeats of this experiment would reduce both systematic errors and random uncertainties.</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p>This was answered correctly by about 73% of candidates.</p>
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<br><hr><br><div class="question">
<p>A student measured the change in mass on heating a sample of calcium carbonate, CaCO<sub>3</sub>(s). What is the mass loss?</p>
<p style="padding-left: 180px;">Mass before heating: 2.347 g ± 0.001<br>Mass after heating: 2.001 g ± 0.001 </p>
<p>A. 0.346g ± 0.001 </p>
<p>B. 0.346g ± 0.002 </p>
<p>C. 0.35g ± 0.002 </p>
<p>D. 0.35g ± 0.001</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">A student heated a solid in a crucible. The student measured the mass of the solid and crucible before and after heating and recorded the results.</p>
<p class="p2">\[\begin{array}{*{20}{l}} {{\text{Mass of crucible and solid before heating}}}&{ = 101.692{\text{ g}}} \\ {{\text{Mass of crucible and solid after heating}}}&{ = 89.312{\text{ g}}} \end{array}\]</p>
<p class="p1">What value should the student record for the mass lost in grams?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>12.4</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>12.38</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>12.380</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>12.3800</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p class="p1">A student measured the mass and volume of a piece of silver and recorded the following values.</p>
<p class="p1" style="text-align: center;"><img src="images/Schermafbeelding_2016-08-26_om_09.09.59.png" alt="N13/4/CHEMI/SPM/ENG/TZ0/30"></p>
<p class="p1">Which value, in \({\text{g}}\,{\text{c}}{{\text{m}}^{ - 3}}\), for the density of silver should the student report in her laboratory notebook?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>10.49</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>10.4900</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>10.5</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>10.500</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p class="p1">C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
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<br><hr><br><div class="question">
<p>What is the best way to minimize the random uncertainty when titrating an acid of unknown strength against a standard solution of sodium hydroxide (<em>ie </em>one of known concentration)?</p>
<p>A. First standardize the sodium hydroxide solution against a standard solution of a different acid.</p>
<p>B. Use a pH meter rather than an indicator to determine the equivalence point.</p>
<p>C. Keep your eye at the same height as the meniscus when reading the burette.</p>
<p>D. Repeat the titration several times.</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p>There was a suggestion that we should have used the phrase “minimize random error” instead of “minimize random uncertainty”. That is a fair point but it didn’t seem to worry the candidates, 75% of whom gave the correct answer. </p>
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<br><hr><br><div class="question">
<p class="p1">Density can be calculated by dividing mass by volume. \(0.20 \pm 0.02{\text{ g}}\) of a metal has a volume of \(0.050 \pm 0.005{\text{ c}}{{\text{m}}^{\text{3}}}\). How should its density be recorded using this data?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>\(4.0 \pm 0.025{\text{ g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>\(4.0 \pm 0.8{\text{ g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>\(4.00 \pm 0.025{\text{ g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>\(4.00 \pm 0.8{\text{ g}}\,{\text{c}}{{\text{m}}^{ - 3}}\)</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>What is the graphical relationship between <em>n</em> and <em>T</em> in the ideal gas equation, <em>pV</em> = <em>nRT</em>, all other variables remaining constant?</p>
<p style="text-align: center;"><img 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"></p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>A student carries out a titration three times and obtains the following volumes: \(3.0 \pm 0.1{\text{ c}}{{\text{m}}^3}\), \(3.2 \pm 0.1{\text{ c}}{{\text{m}}^3}\) and \(3.2 \pm 0.1{\text{ c}}{{\text{m}}^3}\). What is the average volume?</p>
<p>A. \(3.1 \pm 0.1{\text{ c}}{{\text{m}}^3}\)</p>
<p>B. \(3.13 \pm 0.1{\text{ c}}{{\text{m}}^3}\)</p>
<p>C. \(3.1 \pm 0.3{\text{ c}}{{\text{m}}^3}\)</p>
<p>D. \(3.13 \pm 0.3{\text{ c}}{{\text{m}}^3}\)</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>A</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
<p>This was the second most difficult question of the paper. Most candidates answered C or D, not realizing that, if correct, repeats would then increase the uncertainty.</p>
</div>
<br><hr><br><div class="question">
<p class="p1">The heat change in a neutralization reaction can be determined by mixing equal volumes of HCl(aq) and NaOH(aq) of the same concentration in a glass beaker. The maximum temperature change is recorded using an alcohol thermometer.</p>
<p class="p1">What is the biggest source of error in this experiment?</p>
<p class="p1">A. Heat absorbed by the glass thermometer</p>
<p class="p1">B. Random error in the thermometer reading</p>
<p class="p1">C. Heat loss to the surroundings</p>
<p class="p1">D. Systematic error in measuring the volumes of HCl(aq) and NaOH(aq) using burettes</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p class="p1">C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>Which statement about errors is correct?</p>
<p>A. A random error is always expressed as a percentage.</p>
<p>B. A systematic error can be reduced by taking more readings.</p>
<p>C. A systematic error is always expressed as a percentage.</p>
<p>D. A random error can be reduced by taking more readings.</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>D</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>A student performs an acid-base titration using a pH meter, but forgets to calibrate it. Which type of error will occur and how will it affect the quality of the measurements?</p>
<p>A. Random error and lower precision</p>
<p>B. Systematic error and lower accuracy</p>
<p>C. Systematic error and lower precision</p>
<p>D. Random error and lower accuracy</p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>B</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p>The rate of a reaction is studied at different temperatures.</p>
<p>Which is the best way to plot the data?</p>
<p><img src="images/Schermafbeelding_2018-08-10_om_07.47.54.png" alt="M18/4/CHEMI/SPM/ENG/TZ2/30"></p>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br><div class="question">
<p class="p1">\({\text{50 c}}{{\text{m}}^{\text{3}}}\) of copper(II) sulfate solution is measured into a plastic cup using a \({\text{100 c}}{{\text{m}}^{\text{3}}}\) measuring cylinder. Excess zinc powder is added and the temperature rise that occurs is measured with a –10 °C to +110 °C thermometer. The enthalpy change for the reaction is then calculated. Which statement is correct?</p>
<p class="p1">A. <span class="Apple-converted-space"> </span>Systematic error will be reduced by repeating the experiment several times and averaging the results.</p>
<p class="p1">B. <span class="Apple-converted-space"> </span>Random error will be reduced by insulating the plastic cup.</p>
<p class="p1">C. <span class="Apple-converted-space"> </span>Random error will be reduced by using a 50 cm<span class="s1">\(^3\) </span>graduated pipette instead of a measuring cylinder.</p>
<p class="p1">D. <span class="Apple-converted-space"> </span>Systematic error will be increased by using a larger volume of copper(II) sulfate solution.</p>
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<h2 style="margin-top: 1em">Markscheme</h2>
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<p>C</p>
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<h2 style="margin-top: 1em">Examiners report</h2>
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<p class="p1">One respondent remarked that it was good to see a more demanding Q30. This was born out by the statistics where it appeared as the second hardest question on the paper. It is important that lab work breeds familiarity with errors, both random and systematic.</p>
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<p>Graph 1 shows a plot of volume of CO<sub>2</sub>(g) against time for the reaction of CaCO<sub>3</sub>(s) with 1.00 moldm<sup>−3</sup>HCl (aq). The acid is the limiting reagent and entirely covers the lumps of CaCO<sub>3</sub>(s).</p>
<p>Which set of conditions is most likely to give the data plotted in graph 2 when the same mass of CaCO<sub>3</sub>(s) is reacted with the same volume of HCl(aq) at the same temperature?</p>
<p><img style="display: block; margin-left: auto; margin-right: auto;" src="data:image/png;base64,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" alt></p>
<p> </p>
<p><img 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" alt></p>
</div>
</div>
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</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
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</div>
</div>
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</div>
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</div>
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</div>
</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question">
<p>C</p>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question">
[N/A]
</div>
<br><hr><br>