Exam 1 - Grad Stats

What is the relationship between a sample and a population?

"A population is every single person regarding a category. An example is the entire voting population in the U.S. A sample is a portion or a subset of that population, often smaller in size. An example of this is voters in San Francisco, CA. It is not realistic to interview every single voter in the population and would be very time-consuming. Using statistics, an inference can be made about the population by surveying a random sample of the population and analyzing the data. "A population includes every member within a particular group, whereas a sample is a smaller subset of a given population.

Select all items below that represent qualitative variables. 1 number of floors in a skyscraper 2 model of car 3 zip code 4 shoe size

1, 2, 3, 4

What are statistics?

Statistics is the mathematical field that helps us organize, describe, and interpret information or data. Often included are mathematical formulas and calculations to help better understand large sets of information.

Why is it important that extraneous variables be taken into account in an experimental study?

It is important to identify extraneous variables in an experimental study, because Extraneous variables may have an effect on the dependent variable and could skew the results of an experiment.

Describe the important variables involved in an experimental study.

The important variables involved in an experimental study are the independent variable, the dependent variable, control variables, and extraneous variables. Let us say that a researcher is doing an experiment to see whether or not a cold medication helps patients with the flu feel better. The independent variable is the cause, in this case, the cold medication. It is what is directly manipulated by the researcher. The dependent variable is the effect, in this case, whether or not the medication will help the patients feel better. The control variable is a variable that remains unchanged throughout the experiment; in this case, it could be the time and the dosage the medication is given. Lastly, extraneous variables are variables that the researcher is not intentionally examining that may influence the dependent variable. An example of an extraneous variable in this specific example is smoking status. Perhaps, smoking may decrease the amount of relief of the cold medication.

discrete variables

involves counting whole numbers (0,1,2,3) e.g. religion, number of patients in a hospital

continuous variables

involves measuring where the value can take a fraction or decimal values between two numbers e.g. temperature, height, weight, TIME

qualitative variables

variables measured by category or label

quantitative variables

variables measured in numerical terms

You sample the heights of all females on a swim team. You summarize the data by calculating the mean, median, mode, and range. What type of statistical method is used? 1 descriptive statistics 2 sample statistics 3 cause-effect statistics 4 inferential statistics

1 descriptive statistics

Categorizing the sports offered by school districts in your state would be an example of ______________ a(n) scale of measurement. 1 nominal 2 ordinal 3 ratio 4 interval

1 nominal nominal because the measurement "names" or "categorizes" the types of sports.

Select all statements that are true about statistics. 1 Statistics are used to organize and summarize data. 2Statistics are used to interpret information. 3 Statistics allow inferences to be drawn about a sample group. 4 Statistics must be handled responsibly.

1,2,4

Name 3 reasons why statistics are important.

1. Statistics allow data to be described and communicated concisely. For example, it is not feasible for an instructor to report all 35 exam scores for feedback. Instead, the instructor can provide an average so the student can have an idea of how well he or she did. 2. Statistics allow us to draw inferences about data, especially in science. An example is that researchers can study people with depression and the effect of medication by taking a sample of say 1,000 people with depression since it is unrealistic to interview every single person with depression. Further statistical methods can lead to an inference about whether this medication is effective at treating depression. 3. Statistics give us the tools to make everyday decisions. As a consumer of products and information, an understanding of statistics allows us to critically evaluate information presented to us and make responsible decisions. This helps us avoid falling to false information and fake news.

Discuss the differences between descriptive and inferential statistics.

Descriptive statistics include analyzing and summarizing data often with a mean, median, mode, range, variance, and standard deviation. Inferential statistics are performed when researchers want to make an inference about a population based on data from a sample. Calculations include such as hypothesis testing (z-scores and t-tests).

How do researchers use samples to make inferences about a population?

First, the researcher needs to identify a goal, for example, "How long does one need to practice for a U.S. driver to pass the DMV driving test?" Then, they need to identify the population, in this case, drivers taking the drivers test. After, they need to choose a representative sample, say, 10,000 drivers taking their DMV driving test randomly selected from all over the U.S. They would then collect data and survey that sample how much time they practiced driving and then summarize the data. Based on the data gathered from this sample, researchers can then use statistics to make predictions about the population as a whole.

Why is it important that statistical methods are performed ethically?

"It is important that statistical methods are performed ethically, because incorrect data collection, analysis, and interpretation can lead to making false inclusions. This can be dangerous and make inaccurate claims for others to follow that are not grounded in real data analysis. Since statistics often serve as validating claims, such as "90% of dermatologists recommend this sunscreen," making false claims and inaccurate statistical analyses can jeopardize people's health and even increase the risk of health conditions such as skin cancer. For these reasons, it is important that we be ethical and keep in mind accuracy and validity while completing statistical analysis. "Statistics is a very powerful tool that allows large amounts of information to be communicated and understood concisely. However, if statistical methods or data collection are performed unethically or irresponsibly, it can skew the results of a study and very easily mislead others into believing claims that are not necessarily supported by evidence.

Why is it important to understand statistics and ethical conduct of statistical methods?

"It is important to understand statistics, because you should know how to critically evaluate health claims. For example if an ad says "9 out of 10 dermatologists recommend this sunscreen," but the total dermatologist sample size was 10, this may be misleading and not truly representative of all dermatologists. By understanding critics, we have a critical eye. Similarly, it is important to have ethical conduct when completing statistical methods, because statistics are often used to validate health claims. By falsifying data you may make false claims and spread fake statistics, which can mislead others and potentially cause injury or harm. "It is very important that we are equipped to critically evaluate claims that we encounter from any source each day so that we are able to make responsible decisions. By understanding the proper ways statistics can be used and the strengths and limitations of these techniques, we will be much better able to detect false or inaccurate claims and accept claims with adequate empirical support.

Define each of the 4 scales of measurement and provide an example of each.

"NOIR The four scales of measurement are nominal, ordinal, interval, and ratio. The nominal scale is used for qualitative data and refers to variables that involve categories or names. Examples include blood type and room number. The ordinal scale is also used for qualitative data, but in addition to categories, it includes ranking by size or measure. Examples include T-shirt size (small, medium large) and awards (bronze, silver, gold). The interval scale is used for quantitative data and it does not have a meaningful zero and sometimes contains negative values. An example is temperature, where zero does not indicate the absence of temperature, but is in fact a real temperature. The ratio scale is used for quantitative data, where there it does have a meaningful zero. An example is cups of coffee and number of errors on an exam. You can drink 0, 1, 2, 3 and four cups of coffee. Drinking 0 cups of coffee is meaningful in this case. You can also score 0 errors on an exam, which signifies you did well. "- A nominal scale refers to variables that only provide categorical information (Example: political party affiliation). - An ordinal scale provides both categorical information and rank order (Example: class ranking - freshman, sophomore, junior, and senior). - The ratio scale is used for quantitative variables wherein there exists an absolute zero (Example: weight). - The interval scale is employed when the variable does not have an absolute, or meaningful, zero (Example: temperature measured in Fahrenheit).

This is a three-part question. Be sure to label your response for each part. Suppose you want to study the effects of a genetically modified diet on the weight of dogs. Give at least 3 examples of extraneous variables. Explain why the variables are extraneous. Why is important to identify extraneous variables in an experimental study?

1. Three examples of extraneous variables in this experiment is the feeding schedule for the dogs diet, the age of the dogs, and the eye sight of the dogs. 2. Feeding schedule is an extraneous variable, because it may affect whether or not the dogs gain weight. For example, owners who feed the dogs less often, will have dogs that perhaps gain less weight than owners who feed their dogs 5-6 meals a day. The age of the dogs is an extraneous variable, because it may affect weight. For instance, perhaps the older aged dogs (8+ years) may gain more weight than younger age dogs on the same diet. Lastly, the eye sight of the dogs may affect weight gain, because dogs who cannot see may have difficulty eating, in turn, eating less and making them lose weight. 3. It is important to identify extraneous variables in an experimental study , because Extraneous variables may have an effect on the dependent variable and could skew the results of an experiment.

Ranking soccer teams as elite, platinum, gold, and silver would be an example of ______a(n) scale of measurement. 1 nominal 2 ordinal 3 ratio 4 interval

2 ordinal ordinal because the teams are ranked by size or magnitude

A(n) _____________ scale of measure may include variables such as height, weight, reaction time, or gallons of gas in a tank. 1 nominal 2 ordinal 3 ratio 4 interval

3 ratio ratio because you can measure the distance from an absolute zero

The number of hours that a college student studies for final exams is a quantitative variable. Which statement below justifies this variable type? 1 The number of hours is a count of using whole numbers. 2 The number of hours is a label or category. 3 The number of hours is numeric and can be expressed as a decimal or fraction of a whole number. 4 The number of hours a student studies is a characteristic of the student.

3= quantiative ---- 2= qualitative 1= discrete

Which statistical method involves the use of sample data to make predictions about the population in which the sample belongs? 1 descriptive statistics 2 sample statistics 3 cause-effect statistics 4 inferential statistics

4 inferential statistics

A(n) _______________ scale of measure may include variables such as temperature and average rainfall. 1 nominal 2 ordinal 3 ratio 4 interval

4 interval

Describe the differences between qualitative and quantitative data. Provide an example of each.

Quantitative data includes variables involving numbers that is measured as fractions or counted as whole numbers. One example of a quantitative variable is weight in kg, with an example datum of weight of 58.5 kg. In contrast, qualitative data does not involve measuring or counting numbers, but rather involves labels, names, and categories. Examples of qualitative data include eye color of green, female gender, agnostic religion, and the presence of cancer.

What are discrete variables? Continuous variables? Be able to provide an example of each.

Quantitative variables can be either discrete or continuous. Discrete variables can be organized into separate categories or counted using whole numbers. Qualitative variables are discrete variables. Examples of discrete variables include religion and number of people in a classroom. Continuous variables can be broken down into fractions or decimal components. Examples of continuous variables include height, weight, and temperature.