A local school board is about to vote on a year-round schooling initiative. In a bold move, the school board chair decides to poll the crowd for its opinion on the proposed policy and then take that information into account before making a decision. Show Which of the following is true about the sample the school board chair took? A. The sample is, in fact, not a sample, but a census. B. It is a random sample. C. It was not a random sample, but could be treated as random. D. It was not a random sample and is very susceptible to biases from the crowd.  UNIT 1 — MILESTONE 1 Score 27/29 You passed this Milestone 27 questions were answered correctly. 2 questions were answered incorrectly. 1 Select the correct statement regarding experiments. A researcher can carefully control the explanatory variables but not observe human responses. A researcher can ignore explanatory variables and observe human responses. A researcher can carefully control the explanatory variables and observe human responses. A researcher can observe the explanatory variables but not control human responses. RATIONALE The defining part of experimental setting is that the researcher can control the setting and apply some treatment to observe how it affects an outcome of interest. The responses by the participants are not controlled by the researcher. CONCEPT Observational Studies and Experiments 2 A different coffee seller offered to sell coffee to Jenae's company for half the cost of their current brand. Jenae knew her co-workers were really partial to the coffee they drank now, so she decided to conduct a study to see if they noticed the difference in flavor. Her co-workers were convinced they would. Jenae provided each person with a sample and said that some had the new coffee and some did not. Only Jenae knew who had which brand of coffee. Jenae's strategy is an example of a(n) ________. randomized experiment completely randomized experiment blind experiment matched-pair designed experiment RATIONALE Since participants are unaware of what group they are in, regular or new coffee group, this is referred to as blinding in an experiment. CONCEPT Blinding 3 Continuous: Height, weight, annual income. Continuous data (like height) can (in theory) be measured to any degree of accuracy. If you consider a value line, the values can be anywhere on the line. For statistical purposes this kind of data is often gathered in classes (example height in 5 cm classes). Discrete data (like number of children) are parcelled out one by one. On the value line they occupy only certain points. Sometimes discrete values are grouped into classes, but less often. If you're studying for a statistics exam and need to review your data types this article will give you a brief overview with some simple examples. Because let's face it: not many people study data types for fun or in their real everyday lives. So let's dive in. Quantitative vs Qualitative data - what's the difference?In short: quantitative means you can count it and it's numerical (think quantity - something you can count). Qualitative means you can't, and it's not numerical (think quality - categorical data instead). Boom! Simple, right? There's one more distinction we should get straight before moving on to the actual data types, and it has to do with quantitative (numbers) data: discrete vs. continuous data. Discrete data involves whole numbers (integers - like 1, 356, or 9) that can't be divided based on the nature of what they are. Like the number of people in a class, the number of fingers on your hands, or the number of children someone has. You can't have 1.9 children in a family (despite what the census might say). Continuous data, on the other hand, is the opposite. It can be divided up as much as you want, and measured to many decimal places. Like the weight of a car (can be calculated to many decimal places), temperature (32.543 degrees, and so on), or the speed of an airplane. Now for the fun stuff. Qualitative data typesNominal dataNominal data are used to label variables without any quantitative value. Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on. In plain English: basically, they're labels (and nominal comes from "name" to help you remember). You have brown hair (or brown eyes). You are American. Your name is Jane. Examples: What color hair do you have?
What's your nationality?
Notice that these variables don't overlap. For the purposes of statistics, anyway, you can't have both brown and rainbow unicorn-colored hair. And they're only really related by the main category of which they're a part. Ordinal dataThe key with ordinal data is to remember that ordinal sounds like order - and it's the order of the variables which matters. Not so much the differences between those values. Ordinal scales are often used for measures of satisfaction, happiness, and so on. Have you ever taken one of those surveys, like this? "How likely are you to recommend our services to your friends?"
See, we don't really know what the difference is between very unlikely and unlikely - or if it's the same amount of likeliness (or, unlikeliness) as between likely and very likely. But that's ok. We just know that likely is more than neutral and unlikely is more than very unlikely. It's all in the order. Quantitative data typesInterval DataInterval data is fun (and useful) because it's concerned with both the order and difference between your variables. This allows you to measure standard deviation and central tendency. Everyone's favorite example of interval data is temperatures in degrees celsius. 20 degrees C is warmer than 10, and the difference between 20 degrees and 10 degrees is 10 degrees. The difference between 10 and 0 is also 10 degrees. If you need help remembering what interval scales are, just think about the meaning of interval: the space between. So not only do you care about the order of variables, but also about the values in between them. There is a little problem with intervals, however: there's no "true zero." A true zero has no value - there is none of that thing - but 0 degrees C definitely has a value: it's quite chilly. You can also have negative numbers. If you don't have a true zero, you can't calculate ratios. This means addition and subtraction work, but division and multiplication don't. Ratio dataThank goodness there's ratio data. It solves all our problems. Ratio data tells us about the order of variables, the differences between them, and they have that absolute zero. Which allows all sorts of calculations and inferences to be performed and drawn. Ratio data is very similar interval data, except zero means none. For ratio data, it is not possible to have negative values. For instance, height is ratio data. It is not possible to have negative height. If an object's height is zero, then there is no object. This is different than something like temperature. Both 0 degrees and -5 degrees are completely valid and meaningful temperatures. Now that you have a basic handle on these data types you should be a bit more ready to tackle that stats exam. Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started |
Which of the following data types will be continuous Height of a toddler
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