What do we mean by the language of statistics? What is the most difficult part of this language for you?
What is the importance of statistics in your work or schooling? How do you use statistics? How are you a statistic?
The language of statistics is how we communicate data to other people. We use statistics to prove our points with evidence to support our points so we can back up our conclusions. The hardest part about communicating statistics to others is to gather the correct evidence to support your point. An example would be taking a survey to support a point that more people drive their own vehicles to work rather than taking public transportation. Depending on the area of the survey, the results could vary so to prove this point, a survey should be taken in different areas including urban and rural settings.
Statistic’s are important to my field of study, which is web design. When designing a website, you need to make sure it attracts the right audience. Gathering statistics of what your audience is looking for could ease the process of making the site attractive. I believe that I am a statistic in almost any sense. Examples would be people who have decided to return to college or people that live in upstate New York.
I believe that “statistics as a language” is referring to the fact that we can use statistics in order to communicate with others. Like any other language (english, french, spanish, etc) it may be foreign to some and fluent to others. The most difficult part of this language, for me, would be the non-verbal part of statistics. I find it easier to talk about how we are statistics than to look at a chart, spreadsheet, or graph in order to get the same point across.
Aside from this class, I frequently use statistics in my everyday life. I help edit different types of film footage at my internship. I am assigned a certain amount of footage to sort and edit, and the amount I am assigned is derived from an average. Basically, they monitor the amount of footage that each intern accounts for and come up with an average. Outside of work, I used to help coach my nephews basketball team. In order to help the kids understand how important free throws were, I began tallying their percentages and keeping stats. When the kids actually saw the data in front of them, they began taking shooting free throws more seriously.
I deal a lot with statistics where I work. I work for a hospital health insurance plan and every day we are constantly dealing with millions of customers and patients. People who have insurance and people who don’t have insurance that we are trying to join our company. We have deals and specials all the time and different promotions if we get people to join our health plan. So I deal a lot with statistics because we have to make sure that we are not misleading our customers because if we do, that gives us a very bad name. So I’m very used to dealing with statistics when dealing with patients and customers. We even try to have other large hospitals and companies join our insurance and to do that, we have to be very persuading but not too much that it makes our informational statistics invalid. So I think we deal with statistics a lot more than we realize.
Good evening everyone, to me statistics is a whole new game but I can say that it seems interesting. When it comes to statistics language, well this is basically the terminology used for identifying or modeling probabilities and sequences of m words by means of a probability distribution. For example when someone does a poll to find out x information. The language used to me is new therefor I have to say that I have difficulty understanding many concepts of probability. I think that if I keep reading I will find this easier by the day. In my work I can say that statistics are used to identify potential incidents for the year or what are the most related work injuries we have according to our industry. I guess we all use statistics at one point or another I use statistics or statistical information when I want to find out something like the best diet out there or the best weight-loss program there is, I browse the Internet and find the popular diets and look at the reviews compare good and bad then make the decision of choosing one and follow it. At one point or another we are all statistics for example we may already have a poll of how many Hispanic women are in school at age 30 or how many Hispanic women are successful or the top 10 successful women in the US. We are all part of a poll or census information therefor we are all statistic.
Statistics is data that is collected and is analyzed to inform society. Statistics are used everywhere; we see statistics in use every day; statistics is used to answer important and practical questions. Statistics is a collection of mathematical tools and techniques, which are used extensively in research and problem solving to answer questions. Statistical methods play a key role in all branches of science, engineering and economics. To me the hardest part about statistics is statistical thinking. Statistical thinking involves determining whether results are statistically significant. Statistical thinking is so much more than the mere ability to execute complicated calculations. I am statistic because I am a female that is Hispanic and is in the military. Military uses statistics when they speak of the number of casualties each year. We also use statistics when addressing big issues such as sexual assaults and suicide.
In your readings this week, the book discusses misuses of statistics. Many advertisements misuse statistics to their advantage and can get away with this because it’s illegal.
For example, an ad says fly Airline A and receive 20% off. You may think 20% is off the purchase of an airline ticket but 20% could really be off your purchase of a beverage on your next flight. So if it’s not specific as to what the 20% is off of then you are being misled.
Can you think of a case where you find statistics to be misleading?
I remember reading the news recently and saw a story that had to do with toothpaste brands. I can’t remember what exact brand it was (so ill call it brand X), but they were forced to stop saying that 80% of dentists chose brand X. It turns out that the company was misleading the public. When people saw that 80% of dentists were choosing Brand X, it was believed that only 20% of dentists chose other brands. However, it was discovered that during the survey, dentists were able to choose more than 1 brand. In conclusion, other brands could be just as popular as Brand X.
Statistics are usually misleading on a lot of products, but that could just be my personal opinion. I tend to find a lot of brands will advertise their product as “The best product” or “The number 1 recommended brand” but really, how do we know that is truly the case? I just bought tires last October and I specifically remember comparing the difference between Goodyear and Michelin tires. Goodyear had an advertisement of how they were the longest lasting tire and then when you looked at Michelin they said the same thing. So how is a customer really supposed to know which tired lasts the longest? So I personally think a lot of products anymore these days have misleading statistics to make themselves sound better than their competition.
Another example of where statistics can be misleading is with all of these commercials for insurance companies. I cant stand these commercials. Every single one says “People who switched to us saved an average of $450.” How can company A say that they save customers $450 over company B, but also company B says that they can save $400 over company A. I would like to know how and where they get these statistics from, and how they average them out. Is the money saved over 6 months? one year? ten years? If it was true with everything that they are saying, and my insurance company charges me $600 every six months, but a different company says that can save me $450. There is no way that I would have the exact same coverage for $150 every 6 months, compared to the $600.
In 2009 and 2010, Reebok made the following claims about its EasyTone and RunTone shoes: Lab tests “proved that the shoes work your hamstrings and calves up to 11% harder and tone your butt up to 28% more than regular sneakers just by walking!” The Federal Trade Commission investigated the claims. It turns out this is one of the most blatant misleading statistics examples; the only thing the shoes actually did was make it uncomfortable to walk. The FTC forced Reebok to refund over $25 million to consumers.
One of the major misuses of statistics that stuck out to me was regarding the experimentation of penicillin treatment to assist syphilis victims in Tuskegee, Alabama. This omission of critical information was not only misleading, but in the end it was criminally wrong by denying treatment to a select group and harmed many people. It is very important to understand the context and source of the information so as to evaluate if there may be any potential agenda or bias in the study. It is amazing that this abuse went undiscovered or unreported for 27 years.
I believe the information that drug companies give in regard to weight loss products are misleading. For instance, they state that individuals have lost 30 lbs in the months, but those agents on random people. Everyone will not report a loss or gain if they feel it won’t be significant to the study, we know that. Plus it’s impossible one pill to work the same for every person. We don’t hear too often about drug interactions or the inability for the drug to be effective based on prescriptions. What about the individuals who do not exercise regularly or eat to promote weight loss? All those pieces of information are important to share.
Some students have a little trouble with understanding the differences between continuous and discrete data. Can you discuss the differences between discrete and continuous data and use examples of each to support your theory?
Continuous and discrete data can definitely be a little confusing at times. When talking about discrete data, it involves having only a certain number of steps or problems to it. For example, the number of questions on a quiz would be discrete data because there only a certain number of questions and countable number at that. When it comes to continuous data, that means that it has an infinite number of steps. Another example for continuous data would be the amount of time it takes to complete the quiz mentioned earlier. The quiz could take someone 5 minutes or it could take them 20 minutes. It’s a continuum because we don’t really know exactly how long the quiz will take.
Statistics and all that it entails can become very confusing for a person that is not only bad at math but that has no knowledge of the subject. After reviewing the reading for this week, I learned that both discrete data and continuous data derive from Quantitative data. Quantitative data consist of numbers representing counts or measurements. Discrete data can only take certain values and it cannot take values in between. For example, when you go to the store to buy fruit and you decide to buy an apple you cannot but 1/4 of an apple you have to buy the whole thing. Another example is the number of children that live in a household. There cannot be 1 1/2 kids living in a household since a person cannot be just half a person. Continuous data can take on any value on a range. For example, weight of a person it does not have to be whole numbers. A person can weigh 120.5 pounds or even 205. Also, when we set our alarms they can be considered continuous data since we can set it at 7:35 which is not a whole number.
When it comes to discrete and continuous data, it is important to understand the difference. Discrete data is a set number of values that are used in a study with no additional values to support the statistics. Continuous data is data that is collected but does not have a set amount of data to be collected, the collection of data continues till the data suits a theory or disproves it. An example of discrete data would be finding the amount of money a company makes every hour for one day. The data is discrete because there is a set amount of data to be collected in a specific time period. An example of continuous data would be monitoring an individuals heart rate in the hospital. There is no set time and the collected can vary in different time variables depending on the state of the patient.
Discrete data is when you have actual numbers or values that you can count. Continuous data can just continually keep going and can cover a wide range of values. An example of discrete date and continuous data from a football team can be given. Discrete data can be the number of players on each team, or the number of players for each position. Continuous can be a players stats. A quarterback can pass for 1 – infinity amounts of yards in a single season. He is not restricted to a specific (discrete) number for passing yards. The same can go for running backs and receivers.
Discrete data have finite values, or buckets you can count them. Discrete Data can only take certain values. Continuous data technically have an infinite number of steps; continuous data can take any value (within a range). Examples- Discrete: number of children in a household, number of languages a person speaks, number of people sleeping in stats class, the number of students in a class (you can’t have half a student), the results of rolling 2 dice: can only have the values 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12. Continuous: height of children, weight of cars, time to wake up in the morning, speed of the train, a person’s height: could be any value (within the range of human heights), not just certain fixed heights, time in a race: you could even measure it to fractions of a second, a dog’s weight, and the length of a leaf.
After reading the course material, I believe that the difference between discrete vs. continuous data could be simplified down to whether the data values are finite or infinite. An example of discrete data would be to count the number of people that vote in an election (finite). An example of continuous data would be to measure the opinions of those voting in that same election (infinite). Common sense about the content of the data and examining the actual “count” gives us the opportunity to measure the ability to determine which data is discrete and which is continuous.
It doesn’t seem like much of a difference but discrete data is counted while continuous data is measured. Data is discrete if there is a clear separation between the different possible values. If we flip a coin and record the result, there are only two possible values so our observations are discrete. Sets of data involving measurements that can have fractions or decimals are generally continuous. Things like height and temperature would be continuous data. I found after reading that usually, anything that you have to use a measuring device for is continuous data.
Discrete and continuous data are both parts of Quantitative and numerical data and they are representations of counts or measurements. They are numerical only and discreet is a number that can be counted and is a whole number, whereas continuous has an infinite number of values that are not disrupted. It can be anything in that can be measured like volume or length, however with discreet data it has to be an actual number and fractions can not be given like 1.14 tbs or 9.75 size shoe.
Another set of terms that is important for us to understand in statistics is the differences between a sample and a parameter. What is the difference between a sample statistic and a population parameter? Provide an example of each.
“A parameter is a numerical measurement describing some characteristic of a population. A statistic is a numerical measurement describing some characteristic of a sample” (Triola, 2010).
An example of a parameter can be used by drug companies to test their drugs to see if they are effective. In a study of drug X, 1,000 people were given the drug and 850 of the people had their symptoms relieved. This is a parameter because it is based off of 1,000 drug testers.
An example of a statistic would be choosing the winner of this years NCAA basketball tournament. In a poll of people living in the United States, 38% believe that Kentucky will win the NCAA tournament. This is a sample because it is based off of those people who said Kentucky would win the tournament, its not based off everyone in the U.S.
A parameter is a numerical measurement describing some characteristic of a population.
An example of a parameter would be the following:
There are 53 members on the roster of an NFL football team. 50% of the members of the team are defensive players. This is a parameter because it is a based upon the population of the entire team.
A statistic is a numerical measurement describing some characteristic of a sample.
An example of a statistic would be the following:
There are approximately 300 million people living in the United States and of that number, approximately 24% would consider themselves to be Liberal/Progressive voters. Of that group, approximately 10% would vote only for a candidate focused on Environmental issues. This would be considered a statistic because the number is based upon a sample.
I feel that a statistic and a parameter are very similar. In our reading, I learned that parameter is a numerical measurement describing some characteristic of a population. A statistic is a numerical measurement describing some characteristic of a sample. They are both descriptions of groups. The difference between a statistic and a parameter is that statistics describe a sample. A parameter describes an entire population. A sample is a smaller subset that is representative of a larger population. A parameter is a numerical value that states something about the entire population being studied. For example, we may want to know the mean wwingspan of the American bald eagle. This is a parameter, because it is describing all of the population. Statistics is used in studies when it is infeasible or even impossible to study each and every member of the group of interest.
The main difference between a population and sample has to do with how observations are assigned to the data set. A population includes all of the elements from a set of data. A sample on the other hand consists of one or more observations from the population. So therefore, the two of them go hand in hand. It all depends on the sampling method, but sometimes there are times when a sample can have fewer observations than the population, the same number, or more observations. Also, more than one sample can be derived from the same population.A parameter is a characteristic of a population. A statistic is a characteristic of a sample. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.
When referring to the parameter of a population we are referring to a number of subjects by a characteristic such as all individuals who live in Cincinnati Ohio, but when mentioning a sample statistic we are discussing subjects based on a measurable characteristic of that sample such 19-21 year old who are independent living in the downtown Cincinnati area. It’s basically pitting data about the entire population to a sample of that population.
With statistics, graphs are a great way to visualize what is going on with the data that you are analyzing. One of the most popular graphs used in statistics are histograms. What is the purpose of using a histogram? Provide two new examples of when one might be used.
The general purpose of a histogram is to present an easily understood summary about certain data; it can be almost any type of data. The written data is transposed onto a chart that has vertical blocks; the number of blocks depends on the categories of data collected. For example, if you are measuring the frequency of something that occurs in a week you would have seven sections along the horizontal line. The vertical line has numbers indicating how many times the event occurred. Another name for a histogram is a bar chart. The difference between the two is that a bar chart has gaps were as a histogram chart does not have bars between the data. An example is when the vital signs (temperature, pulse, respirations and b/p) are recorded at intervals over a 24-hour period and then assessed overall for stability.
the purpose of a histogram is to show variables that occur over time. It can be used to identify the frequency of an occurrence. You could use a histogram in order to show tips given in a restaurant or the prices of houses sold in a certain area. The histogram creates a picture of the data distribution. The bars would represent the frequency of certain tips or of certain prices of houses.
A histogram is used to analyze the shape of the distribution of data.
The purpose is to create a graphic version of a frequency of distribution.
Example 1: A histogram that would track the frequency in which a taxpayer pays his/her taxes late, measured with the number of times that they are flagged for audit by the Internal Revenue Service.
Example 2: A histogram that would track the annual check up of a males from the age of 40 – 85 on a yearly basis, measured with the likelihood that colon cancer is detected.