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In part (a), many students were unaware that the use of all data was called a census and a common answer was "systematic sampling" and "opportunistic sampling". Most students were able to complete part (b)(i), calculating the lower outlier limit correctly
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Part (a) was answered well though some incorrect responses of “stratified” or “quota sampling” were seen. There were very few responses not using the correct terminology here.
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learners and very few blank responses were seen and parts (e) and (f) provided an opportunity for differentiation. gave a correct conclusion in context. Good attempts to part (f) were rare and blank responses not uncommon. Many answers did not give full and precise definitions and others lacked clarity so that they could have been interpreted as either the sample or a population. For part (i) the information was given in the question but few were able to extract the relevant information: the variables (sun and rain) for May to October (or from the large data set) from 2015. Part (ii) produced a slightly better response with successful comments such as “sun and rain from all of 2015” or “all the variables in the large data set” or “from all across the UK” being seen. Common mistakes often involved describing a population for which the sample was not a subset such as using months from April to November or the year 2016.
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Once again the large data set aspects of this question caused problems for some students but most scored some marks here. the large data set and scoring some marks in part (c). In (i) we were simply looking for an awareness that because the large data set only had data for May to October this would not be representative of the whole population. Some demonstrated a lack of understanding of the principle of using a sample to make inferences by stating that 184 days was not a sufficiently large sample to represent the weather for the whole year. In this case the problem was not the sample size but the fact that the sample was not representative. In (ii) even some of those who had failed to mention May to October in (i) were able to score here by stating that because the missing months were in winter and they expected more rainfall then, the actual mean would be higher. The great storm of October 1987 featured in many incorrect answers to part (c).
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Although some showed a genuine familiarity with the large data set many seemed totally unfamiliar large data set, most correctly identified the sampling technique in part (a) and some drew a sensible inference in part (c) by simply stating that variable A was the most frequent.
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Although some showed a genuine familiarity with the large data set many seemed totally unfamiliar with it and subsequently could not make progress in part (b). For part (b) the students needed to be familiar with the qualitative variables in the large data set (wind speed and cardinal direction were the only choices) and then select an appropriate variable in the knowledge that there were only 3 outcomes. Those who were familiar with the data set would then know that the majority of wind speeds are “light”. Aside from the large data set, most correctly identified the sampling technique in part (a) and some drew a sensible
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Although some showed a genuine familiarity with the large data set many seemed totally unfamiliar inference in part (c) by simply stating that variable A was the most frequent.
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In part (a) many scored the mark by referring to the possibility of bias being introduced by the interviewer or the lack of randomness when using quota sampling. The most common answers that did not score the mark referred to cost or time failing to appreciate that quota sampling is generally quicker and therefore cheaper to implement than simple random sampling. Some thought that quota sampling required a sampling frame which suggested they were confused about the different types of sampling. Part (b) was generally answered
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In part (a) Q4 (a) very few candidates understood about sampling frames and that in order to take a stratified sample one needed to be able to produce a list of the fish.
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In part (b) having been told that a stratified sample was not appropriate, it was slightly surprising that many chose such a sample here. There were very few fully correct answers, with most candidates who selected Quota sampling not mentioning the idea that fish are ignored once the quota for that fish is full.
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This proved to be an accessible start to the paper with most scoring at least 1 mark in part (a) for a partial description of a stratified sample. Very few gained the first mark for clearly describing the method of splitting the population into two groups and labelling these groups. Most realised that a random sample must be taken within each strata and the correct calculation for the number of candidates for each year group was commonly seen.
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This question was answered fairly well. Most students gave a correct response in part (a) and many mentioned quota sampling in part (b). The question required a brief description of the method and
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mentioned quota sampling in part (b). The question required a brief description of the method and although some mentioned “strata” few gave a suggestion as to what these strata might be and a question like this does require the answer to be related to the context of the question. Some mentioned stratified sampling in (b) and gained no credit as this is not a “non-random” method of sampling. Most answered part (c) correctly and parts (d) and (e) were probably the best answered parts on the
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Most answered part (c) correctly and parts (d) and (e) were probably the best answered parts on the whole of section A. Part (f) was a familiar type of question from the legacy S1 specification but,
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