Homework discussion: Introduction to Statistics in Psychological Research
Hello, everyone. Below are some submitted answers to the homework covering material from the Introduction to statistics in psychological research lecture, as well as my commentary. Have a listen and feel free to ask questions in the comments section below.
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Note the particular responses I’ve underlined below. These responses are the ones that stood out as most correct and least problematic. How does your response differ from these (if yours wasn’t selected)? What part of your response could be improved to make it more perfect? (It’s often just one word!) Don’t be discouraged. Responses often are largely correct but ultimately include irrelevant or contradictory or just vague elements that downgrade them. These homeworks are for your benefit to help you understand how to think critically and clearly and to prepare you for the exams and how I’ll grade you, as well. Just imagine if I was deducting points for every little bit that wasn’t perfect! We’re here to learn this summer, so let’s learn from yours and the mistakes of your classmates. Feel free to comment below.
Join the discussion in the Comments section. What answers are better than others and why? Feel free to specify Question # and Response # when you post (Q#R#)!
1. How many punches did you observe in the wrestling video above?
Actual answers from students this term:
- 1. I observed 17 punches during the wrestling video.
- 1. I observed 10 punches in this fight between four different people.
- 1. I counted 16 punches.
- 1. 12
- 1. 14
- 1. 13 punches
- 1. I observed 15 punches.
- 1. 13
- 1. I counted 13 punches.
- 1. 12– most done by Hulk Hogan.
- 1. I saw 11 punches in the wrestling video.
- 1. I only saw 11 punches, I am really interested in how everyone else was seeing so many more!
- 1. 13
- 1) 12
- 1) 10 or 11 punches
- 1. I observed 9 “punches”.
- 1. The number of punches I observed in the wrestling video was 16. This did not include kicks or body slams.
- 1. Eight
- 1. 18 punches
- 1. 12 punches
- 1. I observed 13 punches in the video but was concerned about ‘jabs’ versus ‘punches.’
- 1. I would say there were about 12ish good actually punches, not counting holds, throws, and any other some what of a beating.
- 1. 15
- 1. Not counting all of the head bangs and other moves, 9
- 1. There were 14 punches in the wrestling video shown above.
- 1. I counted 16 punches, I believe. Hulk Hogan was running everywhere!
- 1. I counted 14 punches.
- 1. How many punches did you observe in the wrestling video above? I counted approximately 11 actual punches. I’m not entirely sure if this is correct, as there were many more physical hits, but I did not feel as though they counted as “punches.”
- 1. 11 punches
2. Is this data set (these data) an example of nominal, ordinal, or interval-ratio data? Why?
The following are data collected from a section of an introductory statistics course regarding how many punches were seen in the professional wrestling video. We could regard each section Dr. Ramey teaches as part of a larger ‘group’ of all PSYC 210 students Dr. Ramey teaches.
13, 17, 19, 13, 13, 16, 18, 12, 13, 11, 14, 16, 15, 16, 16, 12, 12, 14
Actual answers from students this term:
- 2. This is an example of interval-ratio data. The intervals between each punch is the same, and there cannot be a negative amount of punches. It would not be nominal data because they are not categories, and it would not be ordinal data because it is not a ranking.
- 2. This data is an example of ratio data. The number of punches have equal intervals, and more importantly have a true zero point.
- 2. I think that for this set of numbers it will be ordinal, because there is no set difference between all of the numbers. Another reason I think this is ordinal is because nominal refers to names, while interval-ratio refers to to ratios, and neither of those are seen in this set of numbers.
- 2. The set of data provided is an example of interval-ratio data. The intervals between data points are equivalent. There is one punch between the data 1 and 2, 2 and 3, 3 and 4, and so on. Also, there can’t be negative values in this set of data because that would indicate negative punches. So 0 equals a complete lack of punches.
- 2. Nominal, this data is in no specific order while represents a specific class section.
- 2. This data is an example of interval-ratio data because the numbers stand for equal amounts of what is being measured (1=one punch, 2=two punches) and the variable has an absolute zero point. 0 indicates a complete absence of the variable (0=zero punches).
- 2. Nominal. Punches don’t have regular intervals. There is either a punch, or there isn’t. We were asked to count the number of punches rather than assign them in a ranking order (ordinal).
- 2. numeric interval-ratio because the difference between 13 and 14 punches is 1 which is just the same as the difference between 15 and 16 punches. It is a ratio because the value of zero indicates an absence of any punches observed at all.
- 2. This data is considered interval-ratio data because the intervals between each value can be equally split unlike ordinal and it is not a category (nominal) based data. So the punches will be counted at exactly 13 punches not 13.5.
- 2. It is interval-ratio because I can say that the difference between 12-15 punches is the same difference between 18-21 punches. So, if someone saw 10 punches that would be twice as much as 5 punches.
- 2. The data is interval-ratio data
- 2. This data is an example of ordinal data because it involves quantitative amounts of data.
- 2. I would say that it is an interval-ratio data set, specifically a ratio one, because in theory someone could have walked away from their computer and seen zero punches. They would be wrong, but it isn’t outside the realm of possibility.
- 2. Interval-ratio. The data are numbers, not nouns and not in not in a specific order
- 2. Ordinal
- 2. This data would be interval-ratio data because it has numeric values that compare the different values of what is being measured without a specific order.
- 2. The data set is an example of ratio data. It is an example of ratio data because you can count the number of punches, there can be a zero data point and the intervals are at an equal distance.
- 2. The given numbers of data collected regarding the number of punches in the video is an example of interval-ratio data. This is because the numbers of punches specify an underlying scale with equal distances or intervals between the points on the scale. The distances are the same no matter where on the number line. Also, there is a non-arbitrary zero point and there cannot be a negative number of punches.
- 2. Interval-ratio data because there is equal distance between the values, there are distinct categories, ranking, and there is a zero point.
- 2. The data set is interval-ratio because the numbers are mutually exclusive, there are equal intervals between each number, and there is an absolute zero – zero would mean no punches, and there is no such thing as negative punches.
- 2. Ratio data
- 2. Based on what we’ve learned about nominal data (that is, data that ‘counts’ or shows frequency) I believe the punches would be nominal data. The order of punches, whether they made contact or who through the punch was not part of the assignment thus, nominal seems most appropriate.
- 2. Nominal variables are names or categories. Ordinal variable or also called Rank Order is numeric variable in which the values are ranked by class or standing finished in something.Interval-ratio is when an equal-interval variable is measured on a ratio scale id it has an absolute zero, meaning the variable indicates a complete absence of the variable. (Example: weight, height, ect.). Well since it would be a group of date of the number of times a person was punched it wouldn’t be Ordinal because its not a standing in a place. It could be Nominal because there are certain categories of people who think he got punch 13, 14, 15 ect. So under each category would be the number of people that said it. It probably couldn’t be Interval because its not a weight or height, it is a number of times being punched but the numbers are all different.
- 2. This data set is nominal because there is no sort of order to it, where as ordinal or interval-ratio have order or some sort of pattern to them.
- 2. This is ordinal data. There is no statistical significance between the data. It is an arbitrary list of numbers (answers to a question) which may be right or wrong. The answers can be rank ordered as correct or incorrect. They could also be ranked: no cigar, close, closer, closest, on the nose.
- 2. This is interval-ratio data (more specifically, ratio data). The interval between 1 punch and 2 punches is the same as between 17 punches and 18 punches (i.e. there are “equal steps along the measured value”). Additionally the point of zero punches is not arbitrary; it signifies that no punches have been observed.
- 2. Interval Data
- 2. I would say that this set is categorized as ordinal data. Classification involves specific quantities.
- 2. This data set is an example of interval-ratio because it uses numerical values as a comparision when measuring.
3. What is a variable, and how does it differ from a constant?
Actual answers from students this term:
- 3. The variable is an characteristic of an object, event, etc. Generally, it is whatever is being changed or manipulated. It is what is being measured. The constant is what the variable is being compared to when being measured. It does not change.
- 3. A variable is something that differs within a population; it varies. On the other hand, a constant is something that is the same across the studied population. For example, if a researcher wanted to know how a certain drug affected the Black American male population, he or she would make sure race and gender were held constant in the population he or she is sampling from.
- 3. A variable can be manipulated and/or can cause change. A constant is unchanging. Weather is a variable changing daily. A house is a constant, never moving, permanently fixed in place unless moved by the variable of the weather.
- 3. The variable changes and a constant stays the same
- 3. As seen in the previous power point slides, it states that variables are any characteristic of some type of organism, an event or even an object. The difference between a variable and a constant is it requires some type of numeric characters to specify as variables do not.
- 3. A variable is something that can change in different circumstances. A constant is something that’s always the same not matter the circumstance.
- 3. A variable is something that changes across multiple samples while a constant stays the same.
- 3. A variable is a characteristic that can have different values whereas a constant cannot have different values for the same characteristic; A constant remains the same.
- 3. The constant is the attribute that cannot change, for example having all men in your study would make “male” a constant (this is forcing a variable to be constant). The variable is the attribute that CAN change and is sought to change within the experiment.
- 3. A variable is a condition or characteristic that can have different values. In other words, it has the capacity to vary.
- 3. A constant is something that never changes throughout the study or observation while a variable is the opposite. It is quantitative or qualitative; it can be a person, number, or category in which can be measured and can vary.
- 3. A variable is something that could change and a constant is something that would stay the same.
- 3. A variable is something that vary’s (like the name might suggest). For example the independent variable is the variable that is manipulated in the experiment to yield the appropriate result, thus it varies across controls. The dependent variable is the result of the experiment that comes from the manipulation of the independent variable so it has variation as well. A constant is something that cannot be changed or manipulated such as age, or gender, or race. Side note: gender and race can technically be surgically modified, but I think they’re still relatively constant.
- 3. A variable is any attribute, property, or characteristic of an organism, event, etc. Variables differ from constants in the fact that variables are subject to change. The independent variable (ex: how much medicine is given) could effect the participants in a different way causing a change or the dependent variable. While a constant is something that remains the same at all times.
- 3. A variable is some characteristic of something that we want to measure. It differs from a constant, in that it can differ. Constants will remain the same, whereas variables can vary either in some sort of measurable scale (ex – number of depressive moods) or will vary across subjects (ex – someone’s gender can’t be changed in an experiment, but it would vary among participants).
- 3. A variable is any characteristic of something that can have different values.. It’s what is being measured. A constant will always remain the same. The constant is used in experiments as a reference point to make observations.
- 3. A variable is is something that can have different values for attributes or characteristics that describe an object, event, or being , whereas a constant has no differences.
- 3. A variable can be any attribute or characteristic of an event; they will not have all the same values or scores. Variables can have variation. Whereas a constant will always be the same, i.e. number of seconds in a minute.
- 3. A variable is something that the researcher is interested in measuring. It can be any attribute, property or characteristic of an organism, object or event. There are many types of variables, such as numeric, categorical, qualitative, quantitative, independent and dependent. Examples of variables include eye color, scores on an exam, or number of classes attended. Variables differ from a constant because in order to be a variable it must vary across the participants in the study. Therefore, not all members of a population or sample will have the same values on the variable.
- 3. A variable has the possibility of being different. A constant does not change.
- 3. A variable is anything you are interested in measuring. It could be any quality/property of an object, organism, or event. It varies depending on the individual, meaning it’s not constant like constants such as the mass of an electron.
- 3. A variable can have different characteristics whereas a constant cannot change.
- 3. A variable simply put is anything that can change (or vary) and variables may have different values for subjects in a study. In contrast, a constant will not change at all during a study and their values should remain the same for all subjects.
- 3. A variable is a characteristic that can have different values. There is discrete and continuous: Discrete is an exact number that can be counted by whole numbers. Continuous means its takes on a infinity of values with some kind of interval, where each value is required to have a infinite number of numeric characters to specify. Time and weight look like discrete but is normally not, its is continuous Something that is constant does not change. So the discrete variable when it talks about weight, weight is always changing so that wouldn’t never be a constant. That would be one way that it would differ from each other
- 3. A variable is changing values and can be manipulated. A constant differs because it is consistent and does not change values.
- 3. As stated above, a variable is any attribute, property, or characteristic of some organism, object, or event. However, it is important to note that variables and constants are different. A variable is a value that is changing/has the ability to change, while a constant is a value that remains unchanged.
- 3. A variable is an attribute, property or characteristic of some organism, object or event. It varies from a constant because it isn’t a set value.
- 3. What is a variable, and how does it differ from a constant? Variables should be different based upon certain data. They are said to “vary,” and are changing or have the potential to change. Constants should remain consistent, and not change in value.
- 3. A variable is any attribute or characteristic of an object or event. A constant however, has no differences.
4. What is the conceptual difference between a sample and a population?
Actual answers from students this term:
- 4. A sample is simply a small portion that is meant to be adequately representative of the population from which it was derived. The population would be the entire portion of the information being observed.
- 4. Random samples are not identical to each other and not identical to the population. A population includes all elements from a set of data while a sample consists of once or more observations from the population.
- 4. A population is the entire set of data that conclusions can be drawn from, while a sample is simply a portion that is representative of the entire population.
- 4. A sample is considered to be representative of a larger population where as the population is the entire group of people a researcher is intending to study. Samples are used to make inferences about a larger population.
- 4. Samples are smaller groups of observation taken from the larger population.
- 4. The conceptual difference between sample and a population is that sample is more focused while the population tends to look at the bigger picture. Your sample can possibly be taken out of your population.
- 4. A sample is a portion of the population. A sample will only reflect some characteristics of the population while the population will reflect all characteristics.
- 4. While a population is the whole, a sample is a part of that whole.
- 4. A sample is a limited set within the population.
- 4. A sample is a subset of a population and is defined by size whereas a population is not defined by size but rather by parameters. A population is the complete set of data that we want to draw inferences from or make conclusions about.
- 4. A sample is not identical to a population, instead it is just a small part or portion of a population.
- 4. The population is everyone that you’re interested in and a sample is a small part of that population that you choose to study.
- 4. The sample refers to the participants of the study, and are often a subset of the population. The population is the larger more broad group. For example in the U.S population different samples could be based off of race, age, gender, etc. Depending on what the researcher was wanting to study. The results from a sample are usually more specific and more reliable than those of the larger population.
- 4. The conceptual difference between sample and population is a sample is smaller than the population. Although, the larger a sample is, the closer it becomes to population.
- 4. A population is made up of everything in a specific set that is of interest, and a sample is a small portion of that set.
- 4. A sample is a portion of a population, a smaller group
- 4. Population is the complete set of data, where a sample is just a portion of a population.
- 4. Samples study part of the population to be able to describe it as a whole, while populations show all possible variables and measurements.
- 4. A sample is taken from a population and different samples taken from the same population can lead to different conclusions about that population. A sample is also a set of observations made from the population. A population is not defined by size and can draw its own data.
- 4. The conceptual difference between a sample and a population is that observations from the sample will be less spread out than the observations in the population. Therefore, the sample is not identical to the population. A population would consist of more items being studied whereas a sample is a smaller subset of that population. However, although the sample has fewer items being studied, it should still represent the same population. You can have a population without having a sample, but you cannot have a sample without having a population.
- 4. A population is every member of the group you are researching. A sample is a smaller portion of that group used for practical statistical research.
- 4. A sample is part of a population; the population is the entire group you’re interested in studying to make conclusions or inferences about.
- 4. A population is the group of people the researcher intends the results of the study to apply. A sample is the scores of that group of people who were studied.
- 4. A sample would be a subset of a larger population that is being studied. The sample should be representative of the population which may be more typical by ensuring it’s a random sample. The population includes all of the subjects in a given study.
- 4. Population is the entire group of people to which the research they are intends to results will apply too. Larger groups to which inferences are made on the basis of the particular set of people (sample) studied. So the difference would be in sample you do not use everyone that could be considered for the studied where as in population you do.
- 4. A sample is a small, statistically significant representation of a larger population. If a geographical population consists of 20,000 people, then 200 or even 20 randomly chosen could represent that total population and the findings of a survey or research project could be significant. Conceptually, a sample should represent the larger population by random association. Twenty – forty people selected randomly at the local grocery store for example, should fairly represent the similarities and differences found throughout the entire local population. However, a population is also any group or subset being studied, for example – smokers, cancer victims, school age children, the elderly.
- 4. A sample is necessarily smaller than a population as a sample is a representative portion of a population.
- 4. A population is the entire set of data that we want to look at, whereas a sample is a smaller section of that population.
- 4. A sample study a certain amount in order to be a representative of the whole population. Populations show all of the potential variables and measurements.
5. Regarding samples and populations, what are some reasons why someone might study (a) a sample and (b) a population?
Actual answers from students this term:
- 5. One may study a sample because they may study a group of individuals in order to come to a conclusion or describe a population. Populations, however, may be studied to study characteristcs of the whole group, as opposed to just a small group of indivuduals.
- 5. a) They might study a sample to get a more focused result, less broad. b) If you are looking for a more broad and open result you would use population to find out your data. (kind of like the bigger picture)
- 5. Someone might study a sample if the population is very large. If someone is studying an immense population, it would be too time consuming and impractical to study the entire thing. However, studying an entire population eliminates any possibility of selection bias showing up in the data.
- 5. a. Someone might study a population to get all of the data and not exclude anything. b. Someone might study a sample if they have limited time, resources, or accessibility to the population.
- 5. a) To gather data and make inferences concerning the whole population. Samples can also be studied to compare individuals within a population. b) Someone may want to determine causation regarding obesity, heart disease. cancer, and endless other conditions that affect the well-being of the population.
- 5. There are times when we realistically cannot spend the time to get a result from every individual in the population (for example, what ever man on earth thinks of Mila Kunitz). In this case, we would narrow our scope to a limited sample. But population can be manipulated – for example, all remaining white rhinos in Africa – and this would be a possible study.
- 5. One might want to study a sample because it is a part of a population from which inferences can be made about the population. A lot smaller, usually. Another reason one might want to study a sample is because of limited resources.For example if you wanted to study people of asian descent with smartphones, you could go out and get SOME people of asian descent with smartphones (sample) rather than get ALL people of asian descent with smartphones (population). One might want to study a population if it is a finite amount. For example, current KU students from Overland Park in the Honors Program studying biology would be a finite population. It is defined by parameters.
- 5. Since a sample is a part of a population, all samples of the same population may have different results so instead of testing the whole population, which could be extremely large depending on what is being studied, then we would want to take a sample of this population for results.
- 5. a) Most of the time you will not be able to do a study on the whole population because of how much time it takes and financial budgets for that particular study. So instead of studying the whole population you then study just a small part. b) If you are studying a whole population then it is most likely going to be a really accurate study. Opposed to a sample, because even though the sample of a population might be accurate there is still a chance that it is not, so studying the population is the most accurate way to go.
- 5. A: a sample: for more specific results, they’re easier to conduct an experiment with, and they take less time than trying to use the population. B: population: gives you results for the group as a whole which makes it easier to draw conclusions from that whole, and sample responses can vary between samples.
- 5. Someone might study a sample to learn about the population. If you take a random sample from people in the population you could take poll on something like do you prefer cats to dogs and ask a certain number of people and collect data. While if you study a population, you could learn something about the population as a whole. A population study is much larger than just taking a sample from that population. That is why it is said that they are not identical.
- 5. Samples are easier to study in that they take less work to run through an experiment. On the other hand, they might not actually be representing the actual population well, or perhaps there is data that can only be gleamed from studying the entire population. I would imagine doing a review of an entire population that is being studied via many samples would be beneficial when you want to ensure the results of the sample experiments, for instance to see if a drug is truly effective.
- 5. There are many reasons why a person would want to use a sample such as wanting to look at something and not being able to get information on the entire population or desiring to try something and not having a large pool of willing participants-such as trying experimental drug. Sometimes researchers are interested in looking at the entire population and how something impacts people
- 5. A sample is more convenient and practical to use when a population is very large and/or difficult to study. A population might be studied in order to obtain more accurate data since the entire set of participants is being studied. A population is a better choice when the st of data of interest is very few.
- 5. People may study a sample to find observations about a certain group of people or events within a population to allow for description of that group. Studies may be done on a population to describe all of the possible outcomes, not just specific, randomly selected ones.
- 5. Someone might study a sample from a population to draw some form of conclusion about said population. If you were to collect data from your population you can use inferential statistics to make a larger point about the world.
- 5. There are many reasons why someone might prefer to study a sample rather than an entire population. For example, studying fewer items means less cost. It would be cheaper to study a portion of the population, or a sample, rather than studying the entire population. Also, it would be quicker to study a sample because there are fewer items to examine. However, some people might prefer to study a population because it could produce more accurate results. Also, it would be more thorough and avoid potential biases.
- 5. One might study a sample because it is easier to access or if they wanted to give a biased interpretation. One may study a population if it is small enough and they don’t want to estimate, so exact data for interpretation.
- 5. Someone might study a sample if they had limited resources, time, or subjects had limited availability. Someone might study a population if the population was small or finite, and participants were readily available.
- 5. a) A sample would be studied so that the researcher can draw conclusions about the whole population. b) Studying a population is good if you want to have accurate results regarding that entire population.
- 5. Regarding samples and population someone may wish to study a sample because it could be impossible to study every single person in a given population. A sample can serve as a subset of the group which is likely to be representative of the population. If a researcher has lots of time, others to assist him and unlimited funds he may choose to study a population. However, a sample if done correctly will provide an accurate representation of the target population making using the sample much more practical and efficient.
- 5. A. For sample it would being able to have everyone is not practical so this makes sample the way to go. B. For population uour study is going to be more accurate because you are using everyone it would impact.
- 5. a) a sample would be used in most situation because it helps researchers draw conclusions based on a smaller group of people. It is particularly useful when making generalizations or predictions. b) a population would be used when a researcher is looking for exact and precise information. The main reason this is method is not usually used is because it is less practical and is not cost effective.
- 5. Scientists study populations to determine attributes of that population. Do smokers cough more? Do pregnant women pee more? Do teenage girls age 13 – 15 trust their mothers or their peers more? However, If the entire population of smokers in Philadelphia is 2 million, that would make for a cumbersome and economically unfeasible study. Randomly conducting surveys in several local malls however or setting up mini clinics to ask smokers questions could help scientists reach a statistically significant but much smaller sample of the smoking population to conduct their research and make inferential conclusions. In this case, the specific population of smokers needs to be studied, but a sample study approach must be taken.
- 5. a. The most prevalent reason to study a sample instead of a population would be feasibility. Often a population is much too large to conduct a study on the entire population. b. On the other hand, if one were to study the population, the data collected could help redefine certain parameters of a population.
- 5. When conducting research, an individual might study a population when it is necessary for them to gather information from an entire set of people for the sake of their study (i.e. the U.S. Census). However, it is often much easier to study a sample in cases where things such as time and resources may be limited.
- 5. a) Someone might study a sample because it is less tedious, smaller numbers and less work than that of a whole population; unless it is a random sample, then it is more like a population, just smaller numbers and less work. b) Someone might study a population to get a better, more accurate result.
- 5) Samples are much more convenient to gather and look at since they are a smaller representation. However, populations give a more complete assessment to the group as a whole.
- 5. While the researcher usually wants to obtain information about the population of interest, it is typically easier to study smaller samples of the population. The population would most likely be studied when feasible; for instance, in a situation where the population is small enough to be managed appropriately. In most other situations, like in those where the researcher wishes to obtain information about a rather large population, he/she would study a sample of the population.
6. What is a random sample, and why is it preferred?
Actual answers from students this term:
- 6. A random sample is where every subject or observation has an equal chance of being picked. The choice of one being picked doesn’t change the likelihood of another one being chosen. Random sampling is preferred because it helps to prevent bias.
- 6. A random sample involves the random drawing of participants in a manner that allows an equal likelihood or participation for all individuals. It is preferred as it helps eliminate any form of bias that could otherwise adversely affect the results of the experiment. Such as the researcher’s own subconscious, or unintentional, bias to control the results in some way.
- 6. It’s a sample that allows every observation of a population a chance of being selected and the choosing of one observation doesn’t change the likelihood of another being chosen. They are good for controlling subjectivity.
- 6. A random sample is where each member of the subset has an equal opportunity to be chosen. The larger the random sample, the more likely it is to represent a population. It is preferred because: there is an equal chance to be chosen and the choice of one observation doesn’t change the likelihood of any other observation.
- 6. A random sample has several characteristics that make it desirable to researchers. Not only does every individual have an equal chance of being chosen, the choice of one individual does not change due to the choice of another individual. Furthermore, random samples are not the same to each other or to the population, but they show a more accurate representation of the population than the larger samples.
- 6. A random sample is when a representative group of the population has been made through random selection from the population. In this type of sampling, each member of the population has an equal likelihood of being selected.
- 6. Random surveys are preferred because they are reliable and replicable. While they are not identical to the population, they do represent the population fairly and accurately the larger the sample. Randomness allows for an equal chance of all opinions being represented but does not increase the likelihood of over-representation.
- 6. A random sample is a method for selecting a sample of a population for a study. It has a basic meaning that every person in a given population has an equal chance of being selected. This method is preferred mainly to avoid skewing the data and making a more realistic population. If people are hand selected it is more than likely going to yield specific results, this could be caused by experimenter bias which should be avoided.
- 6. Random sample is a method in which you select samples that uses truly random procedures (It is usually by using the meaning that each person in the population has an equal chance of being selected.) They normally start with a list of everyone in the population and use a number table. People would preferred this because there is no way that you are only picking people you know that will answer how you want them to. The example from the book say that you wouldn’t want to just give it to your class you would want to randomly select other students to thus giving you better results.
- 6. Random samples provide a researcher a generalization of the population. Random samples provide everyone in the population an equal chance of being selected and helps reduce subjectivity and bias thereby making it the preferred method when conducting a research.
- 6. A random sample is when a sample is selected using truly random procedures where one procedure is for the researcher to begin with a complete list of all the people in the population and pick a group of them to study using a table of random numbers. This is preferred because there will be no bias included in the study.
- 6. A random sample is a sample in which every observation in the population has the same chance of being included. Also, the choice of any of the observations doesn’t affect how likely any other observation will be included. It is preferred because it helps eliminate, or at least control for subjectivity and biases the researcher may have.
- 6. In a random sample every observation in the population has an equal chance of being included. A random sample is preferred to that the sample will have less bias and represent the population better.
- 6. A random sample consists of items or subjects that are randomly selected from a larger population to form a smaller, random sample. With random sampling, the sample is supposed to be representative of the larger population. It is preferred because in random sampling, every observation in the population has an equal chance of being included. Another reason it is preferred is because it controls or eliminates potential researcher biases.
- 6. A random sample is preferred because every observation in the population has an equal chance of being included and a random sample helps control biases and subjectivity.
- 6. Random samples are observations made that had an equal chance of being picked out of an entire population and they aren’t identical to other samples or a population, but as the samples grow, they can become more like a population. They are important because they remove subjectivity form the study.
- 6. A random sample is when the researcher takes a group of participants an then picks participants randomly for the study. It is preferred because you can predict that the conclusions found are applicable to the population.
- 6. A random sample is one where every part of a population has an equal chance of being included. It is preferred because it minimizes the risk of experimenter bias.
- 6. A random sample is something that gives all participants an equal and fair opportunity in whatever is being recorded and are preferred because it can help control biases and subjectivity.
- 6. A sample is considered random if the following characteristics are met: 1) “every observation has the choice of being selected. 2) the choice of another doesn’t affect the chances of another observation.” Dr. Ramey’s ‘“random” monkey and Chuck Norris pictures were not truly random because they were both appealing to him, for some reason! (probably because the monkey picture is adorable!) A random sample is preferred because it helps to ensure that the sample is free from biases!
- 6. It is when you randomly select people to participate in your study from the population. It is preferred over letting people choose themselves because it evens out preexisting differences between the participants. This allows the study sample to be more accurate than it would without the random selection.
- 6. Random samples offer an overall equal chance for observations to be included as well as not disrupting or changing the choice of other observations. This helps eliminate or control biases or subjectivity of the person conducting the study.
- 6. A random sample is a sample in which every observation in the population has an equal chance of being included and also the choice of any one observation does not change the likelihood of the choice of any other observation. Basically a random sample entails objectivity. It tries to get rid of bias and subjectivity.
- 6. A random sample is a collection of samples that are brought together in a totally unrelated way. This can be difficult to achieve. If I pick 20 people “at random”, the thing all those people have in common is that they appealed to me in some way – I chose them. Therefore there is bias. This would create a possibly skewed result in a study that does not show an even coverage of a population or sample.
- 6. A random sample must be selected under the conditions that: every individual within the population has an equal chance of being selected, and the choice of any one individual does not change the likelihood of any other individual being chosen. Randomness is important because it minimizes subjectivity. The more random a sample is (and the larger the sample), the more similar to the population the sample will look. This can enable more accurate data.
- 6. A random sample is a portion of the population that most likely reflects the entire population. Potential biases are eliminated because everyone in the population has an equal chance of being chosen at all times. A random sample is preferred because it provides the most accurate data.
- 6. A random sample is preferred, because it allows the experiment to be random and therefore valid. Like in your lecture, you stated that it was not random because you actually chose it. By choosing things it defeats the purpose of randomness and can skew the data possibly. You want to use random sample to get the best, non bias results possible.
- 6. A random sample is a sample that is not identical to the population or other samples, however it can be more like the population the larger it is. It is preferred because of the equal chance of any given observation to be in the sample.
- 6. A random sample means every observation in the population has and equal chance and the choice of any observation doesn’t change the likelihood of choice of any other. It’s preferred because it’s most accurate. It takes away error
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