PSYC 250 Research Methods I Homework 7 We often want to know things about popula
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PSYC 250 Research Methods I Homework 7 We often want to know things about populations. Examples of populations are the following: 1. All people who shop at Wegmans grocery stores 2. All women serving time in Federal prison 3. Deaf children who sign ASL and also have a cochlear implant However, one problem is that we cannot usually conduct research studies that include entire populations. Such studies are often expensive and very time-consuming. Perhaps more importantly, population studies are usually unnecessary. This is because we can conduct careful studies of samples (drawn from these populations) and make inferences about the populations themselves Put another way, as researchers interested in human behavior, we estimate what we want to know about populations from data we collect from samples. Examples of samples based upon the populations listed above are as follows: 1. People shopping at Pittsford Wegmans on Saturday 29 October between 9 and 11 AM, who agree to complete a survey in return for a $10 Wegmans gift card 2. Female prisoners from the largest three Federal prisons in the US, who agree to take a blood test 3. Deaf children aged 7-10 years attending Texas or Indiana School for the Deaf, whose parents agree for them to participate Ideally, these samples should be random samples from the population. If they are not, then there is the risk that we have a biased sample. This introduces some systematic error into our estimates, due to the sample being non-representative of the population in avoidable ways. Q1: What is the definition of a random sample? Q2: Looking at the examples above, for each one (1-3) discuss one possible way in which the sample might be systematically biased given the population of interest. Assuming that we have a random sample, the only remaining problem is sampling error. Sampling error is non-systematic (random) error that occurs because there is always a chance that our random sample is non-representative of the population it's a random sample, not a perfect sample!Explanation / Answer
Q.8 : Increasing the sample size decreases the width of confidence intervals, because it decreases the standard error.
Q.10 :In statistical inference, one wishes to estimate population parameters using observed sample data.
A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data.
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