How to Write Articles and Essays Quickly and Expertly

From time to time people express amazement at how I can get so much
done. I, of course, aware of the many hours I have idled away doing
nothing, demur. It feels like nothing special; I don't work harder,
really, than most people. Nonetheless, these people do have a point. I
am, in fact, a fairly prolific writer.

Part of it is tenacity. For example, I am writing this item as I wait
for the internet to start working again in the Joburg airport departures
area. But part of it is a simple strategy for writing you essays and
articles quickly and expertly, a strategy that allows you to plan your
entire essay as you write it, and thus to allow you to make your first
draft your final draft. This article describes that strategy.



Begin by writing - in your head, at least - your second paragraph (that
would be the one you just read, above). Your second paragraph will tell
people what your essay says. Some people write abstracts or executive
summaries in order to accomplish this task. But you don't need to do
this. You are stating your entire essay or article in one paragraph. If
you were writing a news article, you would call this paragraph the
'lede'. A person could read just the one paragraph and know what you had
to say.



But how do you write this paragraph? Reporters will tell you that
writing the lede is the hardest part of writing an article. Because if
you don't know what the story is, you cannot write it in a single
paragraph. A reporter will sift through the different ways of writing
the story - the different angles - and find a way to tell it. You,
because you are writing an article or essay, have more options.



You have more options because there are four types of discursive
writing. Each of these types has a distinct and easy structure, and once
you know what sort of writing you are doing, the rest of the article
almost writes itself. The four types of structure are: argument,
explanation, definition, and description. So, as you think about writing
your first paragraph, ask yourself, what sort of article are you
writing. In this article, for example, I am writing a descriptive
article.



These are your choices of types of article or essay:



Argument: convinces someone of something

Explanation: tells why something happened instead of something else

Definition: states what a word or concept means

Description: identifies properties or qualities of things



An argument
is a collection of sentences (known formally as 'propositions') intended
to convince the reader that something is he case. Perhaps you want to
convince people to take some action, to buy some product, to vote a
certain way, or to believe a certain thing. The thing that you want to
convince them to believe is the conclusion. In order to convince people,
you need to offer one or more reasons. Those are the premises. So one
type of article consists of premises leading to a conclusion, and that
is how you would structure your first paragraph.



An explanation
tells the reader why something is the case. It looks at some event or
phenomenon, and shows the reader what sort of things led up to that
event or phenomenon, what caused it to happen, why it came to be this
way instead of some other way. An explanation, therefore, consists of
three parts. First, you need to identify the thing being explained.
Then, you need to identify the things that could have happened instead.
And finally, you need to describe the conditions and principles that led
to the one thing, and not the other, being the case. And so, if you are
explaining something, this is how you would write your first paragraph.



A definition
identifies the meaning of some word, phrase or concept. There are
different ways to define something. You can define something using words
and concepts you already know. Or you can define something by giving a
name to something you can point to or describe. Or you can define
something indirectly, by giving examples of telling stories. A
definition always involves two parts: the word or concept being defined,
and the set of sentences (or 'propositions') that do the defining.
Whatever way you decide, this will be the structure of your article if
you intend to define something.



Finally, a description
provides information about some object, person, or state of affairs. It
will consist of a series of related sentences. The sentences will each
identify the object being defined, and then ascribe some property to
that object. "The ball is red," for example, were the ball is the object
and 'red' is the property. Descriptions may be of 'unary properties' -
like colour, shape, taste, and the like, or it may describe a relation
between the object and one or more other objects.



Organizing Your Writing



The set of sentences, meanwhile, will be organized on one of a few common ways. The sentences might be in chronological order. "This happened, and then this happened," and so on. Or they may enumerate a set of properties ('appearance', 'sound', 'taste', 'small', 'feeling about', and the like). Or they may be elements of a list ("nine rules for good technology," say, or "ten things you should learn"). Or, like the reporters, you may cover the five W's: who, what, where, when, why. Or the steps required to write an essay.



When you elect to write an essay or article, then, you are going to
write one of these types of writing. If you cannot decide which type,
then your purpose isn't clear. Think about it, and make the choice,
before continuing. Then you will know the major parts of the article -
the premises, say, or the parts of the definition. Again, if you don't
know these, your purpose isn't clear. Know what you want to say (in two
or three sentences) before you decide to write.



You may a this point be wondering what happened to the first paragraph.
You are, after all, beginning with the second paragraph. The first
paragraph is used to 'animate' your essay or article, to give it life
and meaning and context. In my own writing, my animation is often a
short story about myself showing how the topic is important to me.
Animating paragraphs may express feelings - joy, happiness, sadness, or
whatever. They may consist of short stories or examples of what you are
trying to describe (this is very common in news articles). Animation may
be placed into your essay at any point. But is generally most effective
when introducing a topic, or when concluding a topic.



For example, I have now concluded the first paragraph of my essay, and
then expanded on it, thus ending the first major part of my essay. So
now I could offer an example here, to illustrate my point in practice,
and to give the reader a chance to reflect, and a way to experience some
empathy, before proceeding. This is also a good place to offer a
picture, diagram, illustration or chart of what you are trying to say in
words.



Like this: the second paragraph sill consist of a set of statements. Here is what each of the four types look like:



Argument:



Premise 1

Premise 2 ... (and more, if needed)

Conclusion



Explanation:



Thing being explained

Alternative possibilities

Actual explanation



Definition:



Thing being defined

Actual definition



Description:



Thing being described

Descriptive sentence

Descriptive sentence (and more, connected to the rest, as needed)



So now the example should have made the concept clearer. You should
easily see that your second paragraph will consist of two or more
distinct sentences, depending on what you are trying to say. Now, all
you need to do is to write the sentences. But also, you need to tell
your reader which sentence is which. In an argument, for example, you
need to clearly indicate to the reader which sentence is your conclusion
and which sentences are your premises.



Indicator Words



All four types of writing have their own indicator words. Let's look at
each of the four types in more detail, and show (with examples, to
animate!) the indicator words.



As stated above, an argument will consist of a conclusion and some
premises. The conclusion is the most important sentence, and so will
typically be stated first. For example, "Blue is better than red." Then a
premise indicator will be used, to tell the reader that what follows is
a series of premises. Words like 'because' and 'since' are common
premise indicators (there are more; you may want to make a list). So
your first paragraph might look like this: "Blue is better than red, because blue is darker than red, and all colours that are darker are better."



Sometimes, when the premises need to be stressed before the conclusion
will be believed, the author will put the conclusion at the end of the
paragraph. To do this, the author uses a conclusion indicator. Words
like 'so' and 'therefore' and 'hence' are common conclusion indicators.
Thus, for example, the paragraph might read: "Blue is darker than red,
and all colours that are darker are better, so blue is better than red."



You should notice that indicator words like this help you understand
someone else's writing more easily as well. Being able to spot the
premises and the conclusion helps you spot the structure of their
article or essay. Seeing the conclusion indicator, for example, tells
you that you are looking at an argument, and helps you spot the
conclusion. It is good practice to try spotting arguments in other
writing, and to create arguments of your own, in our own writing.



Arguments
can also be identified by their form. There are different types of
argument, which follow standard patterns of reasoning. These patterns of
reasoning are indicated by the words being used. Here is a quick guide
to the types of arguments:



Inductive argument:
the premise consists of a 'sample', such as a series of experiences, or
experimental results, or polls. Watch for words describing these sorts
of observation. The conclusion will be inferred as a generalization from
these premises. Watch for words that indicate a statistical
generalization, such as 'most', 'generally, 'usually', 'seventy
percent', 'nine out of ten'. Also, watch for words that indicate a
universal generalization, such as 'always' and 'all'.



A special case of the inductive argument is the causal generalization.
If you want someone to believe that one thing causes another, then you
need to show that there are many cases where the one thing was followed
by the other, and also to show that when the one thing didn't happen,
then the other didn't either. This establishes a 'correlation'. The
argument becomes a causal argument when you appeal to some general
principle or law of nature to explain the correlation. Notice how, in
this case, an explanation forms one of the premises of the argument.



Deductive argument: the premises consist of propositions, and the conclusion consists of some logical manipulation of the premises. A categorical
argument, for example, consists of reasoning about sets of things, so
watch for words like 'all', 'some' and 'none'. Many times, these words
are implicit; they are not started, but they are implied. When I said
"Blue is better than red" above, for example, I meant that "blue is
always better than red," and that's how you would have understood it.



Another type of deductive argument is a propositional argument.
Propositional arguments are manipulations of sentences using the words
'or', 'if', and 'and'. For example, if I said "Either red is best or
blue is best, and red is not best, so blue is best," then I have
employed a propositional argument.



It is useful to learn the basic argument forms, so you can very clearly
indicate which type of argument you are providing. This will make your
writing clearer to the reader, and will help them evaluate your writing.
And in addition, this will make easier for you to write your article.



See how the previous paragraph is constructed, for example. I have
stated a conclusion, then a premise indicator, and then a series of
premises. It was very easy to writing the paragraph; I didn't even need
to think about it. I just wrote something I thought was true, then
provided a list of the reasons I thought it was true. How hard is that?



In a similar manner, an explanation
will also use indicator words. In fact, the indicator words used by
explanations are very similar to those that are used by arguments. For
example, I might explain by saying "The grass is green because it rained
yesterday." I am explaining why the grass is green. I am using the word
'because' as an indicator. And my explanation is offered following the
word 'because'.



People often confuse arguments and explanations, because they use
similar indicator words. So when you are writing, you can make your
point clearer by using words that will generally be unique to
explanations.



In general, explanations are answers to 'why' questions. They consider
why something happened 'instead of' something else. And usually, they
will say that something was 'caused' by something else. So when offering
an explanation, use these words as indicators. For example: "It rained
yesterday. That's why the grass is green, instead of brown."



Almost all explanations are causal explanations, but in some cases (especially when describing complex states and events) you will also appeal to a statistical explanation.
In essence, in a statistical explanation, you are saying, "it had to
happen sometime, so that's why it happened now, but there's no reason,
other than probability, why it happened this time instead o last time or
next time." When people see somebody who was killed by lightening, and
they say, "His number was just up," they are offering a statistical
explanation.



Definitions are
trickier, because there are various types of definition. I will
consider three types of definition: ostensive, lexical, and implicit.



An 'ostensive' definition is an
act of naming by pointing. You point to a dog and you say, "That's a
dog." Do this enough times, and you have defined the concept of a dog.
It's harder to point in text. But in text, a description amounts to the
same thing as pointing. "The legs are shorter than the tail. The colour
is brown, and the body is very long. That's what I mean by a 'wiener
dog'." As you may have noticed, the description is followed by the
indicator words "that's what I mean by". This makes it clear to the
reader that you are defining by ostension.



A 'lexical' definition is a
definition one word or concept in terms of some other word or concept.
Usually this is describes as providing the 'necessary and sufficient
conditions' for being something. Another way of saying the same thing is
to say that when you are defining a thing, you are saying that 'all and
only' these things are the thing being defined. Yet another way of
saying the same thing is to say that the thing belongs to such and such a
category (all dogs are animals, or, a dog is necessarily an animal) and
are distinguished from other members in such and such a way (only dogs
pant, or, saying a thing is panting is sufficient to show that it is a
dog).



That may seem complicated, but the result is that a lexical definition
has a very simply and easy to write form: A (thing being defined) is a
type of (category) which is (distinguishing feature). For example, "A
dog is an animal that pants."



This sentence may look just like a description, so it is useful to
indicate to the reader that you are defining the term 'dog', and not
describing a dog. For example, "A 'dog' is defined as 'an animal that
pants'." Notice how this is clearly a definition, and could not be
confused as a mere description.



The third type of definition is an implicit
definition. This occurs when you don't point to things, and don't place
the thing being defined into categories, but rather, list instances of
the thing being defined. For example, "Civilization is when people are
polite to each other. When people can trust the other person. When there
is order in the streets." And so on. Or: "You know what I mean. Japan
is civilized. Singapore is civilized. Canada is civilized." Here we
haven't listed necessary and sufficient conditions, but rather, offered
enough of a description as to allow people to recognize instances of
'civilization' by their resemblance to the things being described.



Finally, the description
employs the 'subject predicate object' form that you learned in school.
The 'subject' is the thing being described. The 'predicate' is
something that is true of the subject - some action it is undertaking,
or, if the predicate is 'is', some property that it possesses. And the
'object' may be some other entity that forms a part of the description.



As mentioned, the sentences that form a description are related to each
other. This relation is made explicit with a set of indicator words. For
example, if the relation is chronological, the words might be
'first'... 'and then'... 'and finally'...! Or, 'yesterday'... 'then
today'... 'and tomorrow'...



In this essay, the method employed was to identify a list of things -
argument, explanation, definition, and description - and then to use
each of these terms in the sequence. For example, "An argument will
consist of a ..." Notice that I actually went through this list twice,
first describing the parts of each of the four items, and then
describing the indicator words used for each of the four items. Also,
when I went through the list the second time, I offered for each type of
sentence a subdivision. For example, I identified inductive and
deductive arguments.



Summary



So, now, here is the full set of types of things I have described (with indicator words in brackets):



Argument (premise: 'since', 'because'; conclusion: 'therefore', 'so')

Deductive

Categorical ('all', 'only', 'no', 'none', 'some')

Propositional ('if', 'or', 'and')

Inductive

Generalization ('sample', 'poll', 'observation')

Statistical ('most', 'generally, 'usually', 'seventy percent', 'nine out of ten')

Universal ('always' and 'all')

Causal ('causes')



Explanation ('why', 'instead of')

Causal ('caused')

Statistical ('percent', 'probability')



Definition ('is a', 'is defined as')

Ostensive ( 'That's what I mean by...' )

Lexical ('All', 'Only', 'is a type of', 'is necessarily')

Implicit ('is a', 'for example')



Description

Chronology ('yesterday', 'today')

Sensations ('seems', 'feels', 'appears', etc.,)

List ('first', 'second', etc.)

5 W's ('who', 'what', 'where', 'when', 'why')



Complex Forms



As you have seen in this article, each successive iteration (which has
been followed by one of my tables) has been more and more detailed. You
might ask how this is so, if there are only four types of article or
essay.



The point is, each sentence in one type of thing might be a whole set of
sentence of another type of thing. This is most clearly illustrated by
looking at an argument.



An argument is a conclusion and some premises. Like this:



Statement 1, and

Statement 2,

Thus,

Statement 3



But each premise might in turn be the conclusion of another argument. Like this:



Statement 4, and

Statement 5,

Thus,

Statement 1



Which gives us a complex argument:



Statement 4, and

Statement 5,

Thus, Statement 1

Statement 2

Thus Statement 3



But this can be done with all four types of paragraph. For example, consider this:



Statement 1 (which is actually a definition, with several parts)

Statement 2 (which is actually a description)

Thus,

Statement 3



So, when you write your essay, you pick the main thing you want to say. For example:



Second paragraph:



Statement 1, and

Statement 2

Thus

Statement 3



Third paragraph:



Statement 4 (thing being defined)

Statement 5 (properties)

Statement 1 (actual definition)



Fourth Paragraph



Statement 5 (first statement of description)

Statement 6 (second statement of description)

Statement 2 (summary of description)



As you can see, each simple element of an essay - premise, for example -
can become a complex part of an essay - the premise could be the
conclusion of an argument, for example.



And so, when you write your essay, you just go deeper and deeper into the structure.



And you may ask: where does it stop?



For me, it stops with descriptions - something I've seen or experienced,
or a reference to a study or a paper. To someone else, it all reduces
to definitions and axioms. For someone else, it might never stop.



But you rarely get to the bottom. You simply go on until you've said
enough. In essence, you give up, and hope the reader can continue the
rest of the way on his or her own.



And just so with this paper. I would now look at each one of each type
of argument and explanation, for example, and identify more types, or
describe features that make some good and some bad, or add many more
examples and animations.



But my time is up, I need to board my flight, so I'll stop here.



Nothing fancy at the end. Just a reminder, that this is how you can
write great articles and essays, first draft, every time. Off the top of
your head.

Read More

Research-based proof that students use cell phones for LEARNING

A new study conducted by TRU provides a body of research which supports the idea that students use cell phones to learn, and also that schools are not acknowledging or supporting them fully, yet. This research supports the work of  innovative educators who are guiding today’s generation text and will help in the effort of getting more schools to stop fighting and start embracing student use of mobile devices for learning in school. Rather than banning, the study highlights the fact that if we meet children where they are we can leverage their use of mobile devices for powerful lear

ning.

The research supports the fact that mobile technology can inspire and engage students by letting them lead their learning and supporting them in choosing and using the devices they know, love, and prefer. The study reveals that whether allowed to use their devices in school or not, students are moving forward and using them for learning even if their school is lagging behind in embracing student-owned devices.

Kids FINALLY have a case for why they really need mobile devices to learn. The survey is the first of its kind and examines how middle school students are using mobile devices, revealing that these tools are actually helping kids learn math and science better, and increasing their confidence and motivation, despite the fact that most schools (88%) strictly forbid their use for learning.

Despite the perception by some parents and teachers that cell phones are distracting to kids, this national study proves that children deserve more credit as 1 in 3 are using their devices to complete homework and learn better.

Here are some of the most exciting findings from the study:

• "An unexpected number of middle school students (from all ethnicities and incomes) say they are using mobile devices including smartphones and tablets to do their homework. Previous TRU research indicated that middle school students are using smartphones and tablets for communication and entertainment. However, this is the first TRU research that shows that middle school students are also using these mobile devices to complete homework assignments.
• More than one out of three middle school students report they are using smartphones (39%) and tablets (31%) to do homework.
• More than 1 in 4 students ( 26 %) are using smartphones for their homework, weekly or more.
• Hispanic and African American middle school students are using the smartphones for homework more than Caucasian students. Nearly one half of all Hispanic middle school students (49%) report using smartphones for homework. Smartphone use for homework also crosses income levels with nearly one in three (29%) of students from the lowest income households reporting smartphone usage to do their homework assignments.  (a quota was set to ensure a minimum of 200 respondents with a household income of $25,000 or less.)
• Despite the high numbers of middle school students using laptops, smartphones and tablets for homework, very few are using these mobile devices in the classroom, particularly tablets and smartphones. A large gap exists between mobile technology use at home and in school.
• Where 39% of middle school students use smartphones for homework, only 6% report that they can use the smartphone in classroom for school work. There is also a gap in tablet use. Although 31% of middle school students say they use a tablet for homework, only 18% report using it in the classroom.
• 66% of students are not allowed to use a tablet for learning purposes in the classroom, and 88% are not allowed to use a phone.
• Students say using mobile devices like tablets makes them want to learn more.

• A significant opportunity appears to exist for middle schools to more deeply engage students by increasing their use of mobile devices in the classroom.
• Access to mobile devices at home is high among this group, and students are already turning to these devices to complete homework assignments. Therefore, it is only natural and highly beneficial for students to extend this mobile device usage into the classroom.
• Teacher education and training on the effective integration of mobile technologies into instruction may provide significant benefits for all. Mobile device usage in class appears to have the potential to sustain, if not increase interest in STEM subjects as students progress into high school.

It’s time to spread the world and ensure educators know the wealth of ways to safely, ethically, and effectively utilize the power of mobile technology with students for homework and IN the classroom. For ideas and support in using cell phones for learning check out Teaching Generation Text: Using Cell Phones to Enhance Learning.

Survey Methodology

Verizon Foundation

commissioned TRU to conduct quantitative research on middle school students’ use of technology.  TRU conducted 1,000 online interviews among sixth- to eighth-grade students, ages 11-14, yielding a margin of error of + 3.0 percentage points. A quota was set to ensure a minimum of 200 respondents with a household income of $25,000 or less.  Unless otherwise noted, all reported data is based on a statistically reliable base size of n=100 or greater.
TRU is the global leader in youth research and insights, focusing on tweens, teens and twenty-somethings. For more than 25 years, TRU has provided the insights that have helped many of the world's most successful companies and organizations develop meaningful connections with young people. As an advocate for young people, TRU has provided critical direction for many of the nation’s most prominent and successful social-marketing campaigns, helping to keep young people safe and healthy. TRU’s work has made a difference – from being put to use at the grass-roots level to being presented at the very highest levels of government.

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Statistical Analysis of Data

Slide 1

What is statistics?

Latin “status”---political state—info useful to state (size of population, armed forces etc)

A branch of mathematics concerned with understanding and summarizing collections of numbers

A collection of numerical facts systematically arranged

Slide 2

Descriptive Statistics

Statistics which describe attributes of a sample or population.

Includes measures of central tendency statistics (e.g., mean, median, mode), frequencies, percentages. Minimum, maximum, and range for a data set, variance etc.

Organize and summaries a set of data

Slide 3

Inferential Statistics

Used to make inferences or judgments about a larger population based on the data collected from a small sample drawn from the population.

A key component of inferential statistics is the calculation of statistical significance of a research finding.

1.         Involves

·       Estimation

·       Hypothesis  Testing

2.         Purpose

·       Make Decisions About Population Characteristics

Slide 5

Key Terms

1.         Population (Universe)

All Items of Interest

2.         Sample

Portion of Population

3.         Parameter

Summary Measure about Population

4.         Statistic

Summary Measure about Sample

Slide 6

Key Terms

Parameter:

A characteristic of the population.  Denoted with Greek letters such as m or

.

Statistic:

A characteristic of a sample.  Denoted with English letters such as X or S.

Sampling Error:

Describes the amount of error that exists between a sample statistic and corresponding population parameter

Slide 7

Slide 8

Some Notations…

Population

All items under consideration by researcher

m = population mean

s = population standard

      deviation

N = population size

p = population percentage

Sample

A portion of the population selected for study

x = sample mean

s = sample standard  

      deviation

n = sample size

p = sample percentage

Slide 9

Descriptive & Inferential Statistics (DS & IS)

DS gather information about a population characteristic (e.g. income) and describe it with a parameter of interest (e.g. mean)

IS uses the parameter to test a hypothesis pertaining to that characteristic. E.g.

    Ho: mean income = UD 4,000

    H1: mean income < UD 4,000)

The result for hypothesis testing is used to make inference about the characteristic of interest (e.g. Americans ® upper middle income)

Slide 10

Examples of Descriptive and Inferential Statistics

Descriptive Statistics                                      Inferential Statistics

·       Graphical                                                  *   Confidence interval

-Arrange data in tables                                    *   Margin of error

-Bar graphs and pie charts                  *   Compare means of two samples

·       Numerical                                                      - Pre/post scores

-Percentages                                             - t Test

-Averages                                            *   Compare means from three samples

-Range                                                      - Pre/post and follow-up

·       Relationships                                                 - ANOVA = analysis of variance

-Correlation coefficient                            - Levels of Measurement

-Regression analysis

 Slide 11

Another characteristic of data, which determines which statistical calculations are meaningful

Nominal: Qualitative data only;  categories of names, labels, or qualities; Can’t be ordered (i.e, best to worst)    ex: Survey responses of Yes/No

Ordinal: Qualitative/quantitative; can be ordered, but no meaningful subtractions:   ex. Grades A, B, C, D, F

Interval: Quantitative only; meaningful subtractions but not ratios, zero is only a position (not “none”)            ex: Temperatures

Ratio: Quantitative only, meaningful subtractions and ratios; zero represents “none”    ex. Weights of babies

Slide 12

Measures of Central Tendency

“Say you were standing with one foot in the oven and one foot in an ice bucket.  According to the average, you should be perfectly comfortable.”

The mode – applies to ratio, interval, ordinal or nominal scales.

The median – applies to ratio, interval and ordinal scales

The mean – applies to ratio and interval scales

Slide 13

Measuring Variability

Range:  lowest to highest score

Average Deviation:  average distance from the mean

Variance:  average squared distance from the mean

Used in later inferential statistics

Standard Deviation:  square root of variance

expressed on the same scale as the mean

Slide 13

Parametric statistics

Statistical analysis that attempts to explain the population parameter using a sample

E.g. of statistical parameters: mean, variance, std. dev., R2, t-value, F-ratio, rxy, etc.

It assumes that the distributions of the variables being assessed belong to known parameterized families of probability distributions

Slide 14

Frequencies and Distributions

Frequency-A frequency is the number of times a value is observed in a distribution or the number of times a particular event occurs.

Distribution-When the observed values are arranged in order they are called a rank order distribution or an array. Distributions demonstrate how the frequencies of observations are distributed across a range of values.

The Mode

Defined as the most frequent value (the peak)

·       Applies to ratio, interval, ordinal and nominal scales

·       Sensitive to sampling error (noise)

·       Distributions may be referred to as uni modal, bimodal or multimodal, depending upon the number of peaks

The Median

• Defined as the 50th percentile
• Applies to ratio, interval and ordinal scales
• Can be used for open-ended distributions

The Mean

Applies only to ratio or interval scales

Sensitive to outliers

How to find?

Mean – the average of a group of numbers.

2, 5, 2, 1, 5

Mean = 3

Mean is found by evening out the numbers

2, 5, 2, 1, 5

2, 5, 2, 1, 5

2, 5, 2, 1, 5

mean = 3

How to Find the Mean of a Group of Numbers

Step 1 – Add all the numbers.

8, 10, 12, 18, 22, 26

8+10+12+18+22+26 = 96

Step 2 – Divide the sum by the number of addends.

8, 10, 12, 18, 22, 26

8+10+12+18+22+26 = 96

How many addends are there?

Step 2 – Divide the sum by the number of addends.

                                            16

The mean or average of these numbers is 16.

8, 10, 12, 18, 22, 26

What is the mean of these numbers?

7, 10, 16

11

26, 33, 41, 52

38

Median
is in the
Middle

Median – the middle number in a set of ordered numbers.

1, 3, 7, 10, 13

Median = 7

How to Find the Median in a Group of Numbers

Step 1 – Arrange the numbers in order from least to greatest.

21, 18, 24, 19, 27

18, 19, 21, 24, 27

Step 2 – Find the middle number.

21, 18, 24, 19, 27

18, 19, 21, 24, 27

This is your median number.

Step 3 – If there are two middle numbers, find the mean of these two numbers.

18, 19, 21, 25, 27, 28

When to use this measure?

With a non-normal distribution, the median is appropriate

21+ 25 = 46

What is the median of these numbers?

16, 10, 7

7, 10, 16

10

29, 8, 4, 11, 19

4, 8, 11, 19, 29

11

31, 7, 2, 12, 14, 19

2, 7, 12, 14, 19, 31                      13

12 + 14 = 26                            2) 26

                                                        26

Mode
is the most
Popular

Mode – the number that appears most frequently in a set of numbers.

1, 1, 3, 7, 10, 13

Mode = 1

How to Find the Mode in a Group of Numbers

Step 1 – Arrange the numbers in order from least to greatest.

21, 18, 24, 19, 18

18, 18, 19, 21, 24

Step 2 – Find the number that is repeated the most.

21, 18, 24, 19, 18

18, 18, 19, 21, 24

Which number is the mode?

1, 2, 2, 9, 9, 4, 9, 10

1, 2, 2, 4, 9, 9, 9, 10

9

When to use this measure?

If your data is nominal, you may use the mode and range

Using all three measures provides a more complete picture of the characteristics of your sample set.

Measures of Variability (Dispersion)

Range – applies to ratio, interval, ordinal scales

Semi-interquartile range – applies to ratio, interval, ordinal scales

Variance (standard deviation) – applies to ratio, interval scales

Understanding the variation

The more the data is spread out, the larger the range, variance, SD and SE (Low precision and accuracy)

The more concentrated the data (precise or homogenous), the smaller the range, variance, and standard deviation (high precision and accuracy)

If all the observations are the same, the range, variance, and standard deviation = 0

None of these measures can be negative

Two distant means with little variations are more likely to be significantly different and vice versa

Range

Interval between lowest and highest values

Generally unreliable – changing one value (highest or lowest) can cause large change in range.

Range
is the distance
Between

Range – the difference between the greatest and the least value in a set of numbers.

1, 1, 3, 7, 10, 13

Range = 12

What is the range?

22, 21, 27, 31, 21, 32

21, 21, 22, 27, 31, 32

32 – 21 = 11

How to Find the Range in a Group of Numbers

Step 1 – Arrange the numbers in order from least to greatest.

21, 18, 24, 19, 27

18, 19, 21, 24, 27

Step 2 – Find the lowest and highest numbers.

21, 18, 24, 19, 27

18, 19, 21, 24, 27

Step 3 – Find the difference between these 2 numbers.

18, 19, 21, 24, 27

27 – 18 = 9

The range is 9

Mid-range: Average of the smallest and largest observations

Measure of relative position

Percentiles and Percentile Ranks

Percentile:  The score at or below which a given % of scores lie.

Percentile Rank:  The percentage of scores at or below a given score

Mid-hinge: The average of the first and third quartiles.

Quartiles:

Observations that divide data into four equal parts.

First Quartile (Q1)

Semi-Interquartile Range

The interquartile range is the interval between the first and third quartile, i.e. between the 25th and 75th percentile.

The semi-inter quartile range is half the interquartile range.

Can be used with open-ended distributions

Unaffected by extreme scores

Example1: the third quartile of students in the Biometry class = ¾ X 36 = 27th item

Example 2: 60th percentile of the class would be 60/100*36 = 21.6 = 22nd item (round off)

Inter-quartile range/deviation

(Mid-spread): Difference between the Third and the First Quartiles, therefore, considers data of central half and ignores the extreme values

Inter-quartile Range = Q3 - Q1

Quartile deviation = (Q3 - Q1)/2

Quartile Deviation

Measures the dispersion of the middle 50% of the distribution

-- rank the data

-- calculate upper and lower quartiles (UQ & LQ)

             Number Sample          sorted Values                         

            1          25                   

            2          27                              

            3          20                              

            4          23                               

            5          26                               

            6          24       

            7          19                               

            8          16                               

            9          25                               

            10        18                               

            11        30                   

            12        29                               

            13        32                               

            14        26                               

            15        24                               

            16        21                   

            17        28                               

            18        27                               

            19        20                               

            20        16                               

            21        14

             

Number               Sample     Sorted Values     Ranked Values           

            1                                  25        14                   

            2                                  27        16                   

            3                                  20        16                   

            4                                  23        18                   

            5                                  26        19                   

            6                                  24        20                   

            7                                  19        20                   

            8                                  16        21                   

            9                                  25        23                   

            10                                18        24                   

            11                                30        24                   

            12                                29        25                   

            13                                32        25                   

            14                                26        26                   

            15                                24        26                   

            16                                21        27                   

            17                                28        27                   

            18                                27        28                   

            19                                20        29                   

            20                                16        30                   

            21                                14        32       

           

 Number       Sample    sorted Values  Ranked Values           

            1          25        14        LL       

            2          27        16                   

            3          20        16                   

            4          23        18                   

            5          26        19                   

            6          24        20        LQ or Q1        

            7          19        20                   

            8          16        21                   

            9          25        23                   

            10        18        24                   

            11        30        24        Md or Q2       

            12        29        25                   

            13        32        25                   

            14        26        26                   

            15        24        26                   

            16        21        27        UQ or Q3       

            17        28        27                   

            18        27        28                   

            19        20        29                   

            20        16        30                   

            21        14        32        UL

Variance

Variance is the average of the squared deviations

Closely related to the standard deviation

In order to eliminate negative sign, deviations are squared (squared units e.g. m2)

v = s2

Variance (for a sample)

Steps:

Compute each deviation

Square each deviation

Sum all the squares

Divide by the data size (sample size) minus one: n-1

Example of Variance

Variance = 54/9 = 6

It is a measure of “spread”.

Notice that the larger the deviations (positive or negative) the larger the variance

Population Variance and Standard Deviation

Sample Variance and Standard Deviation

The standard deviation

It is defines as the square root of the variance

Standard deviation (SD):

Positive square root of the variance

                        SD = + √ S(y-ў)2÷ n

Variance and standard deviation are

useful for probability and hypothesis testing, therefore, is widely used unlike mean deviation

Population parameters and sample statistics

If we are working with samples, the calculation under-estimates the variance and SD which is biased

Therefore, instead of using n, n-1 (degrees of freedom) is used for sample, e.g.

Standard Deviation

example

Example:  {4, 7, 6, 3, 8, 6, 7, 4, 5, 3}

Measure of relationship

correlation

Definitions

Correlation is a statistical technique

that is used to measure a relationship

between two variables.

Correlation requires two scores from

each individual (one score from each

of the two variables)

Correlation Coefficients

A correlation coefficient is a statistic that indicates the strength & direction of the relationship b/w 2 variables (or more) for 1 group of participants

Another definition – specifically for Spearman’s rho:

Spearman’s correlation coefficient is a standardized measure of the strength of relationship b/w 2 variables that does not rely on the assumptions of a parametric test (nonparametric data)

Uses Pearson’s correlation coefficient performed on data that have been converted into ranked scores

                                                                                   

Distinguishing Characteristics of
Correlation

Correlation procedures involve one

sample containing all pairs of X and Y

scores

Neither variable is called the

independent or dependent variable

Use the individual pair of scores to

create a scatter plot

The scatter plot

Correlation and causality

The fact that there is a relationship

    between two variables does not mean that

    changes in one variable cause the

    changes in the other variable.

A statistical relationship can exist even though one variable does not cause or influence the other.

Correlation research cannot be used to

    infer causal relationships between two variables

in the following examples

�� example 1 - correlation coefficient =1

�� example 2 - correlation coefficient =-1

�� example 3 - correlation coefficient =0

�� the correlation coefficient for the parametric case is called the Pearson product moment correlation coefficient (r)

example 1

paired values

A   3   6   9   12   15

B   1   2   3     4      5

�� variable A (income of family) (1000  pounds)

�� variable B (# of cars owned)

�� here is a perfect and positive correlation as one variate increases in precisely the same proportion as the other variate increases

example 2

paired values

A   3   6   9   12   15

B   5   4   3    2      1

�� variable A (income of family) (100

pounds)

�� variable B (# of children)

�� here is a perfect and negative correlation as one variate decreases in precisely the same proportion as the other variate increases

example 3

paired values

A   3   6  9   12  15

B   4   1  3    5    2

variable A (income of family)

 variable B (last number of postal code)

 here there is almost no correlation because one

variate does not systematically change with the

other. Any association is caused by A and B being

randomly distributed

Correlation coefficients provide a single numerical value to represent the relationship b/w the 2 variables

Correlation coefficients ranges -1 to +1

-1.00 (negative one) a perfect, inverse relationship

+1.00 (positive one) a perfect, direct relationship

  0.00 indicates no relationship                      

Graphic Representations of Correlation

The form of the relationship

In a linear relationship as the X scores increase the Y scores tend to change in one direction only and can be summarised by a straight line

In a non-linear or curvilinear relationship as the X scores change the Y scores do not tend to only increase or only decrease: the Y scores change their direction of change

Computing a correlation

Alternative Formula for the Correlation Coefficient

Computing a Correlation

Non-linearity

2 Types of Correlation Coefficient Tests

1)Pearson r

Full name is “Pearson product-moment correlation coefficient”

r (lower case r & italicized) is the statistic (fact/piece of data obtained from a study of a large quantity of num. data) for this test

2)Spearman’s rho

Full name is “Spearman’s rank-order correlation coefficient”

rho (lower case rho & italicized) is the statistic for this test

Correlation Coefficients & Strength

Strength of relationship is one thing a correlation coefficient test can tell us

Rule of Thumb for strength size (generally)

A correlation coefficient (r or rho)

Value of 0.00 indicates “no relationship”

Values b/w .01 & .24 may be called “weak”

Values b/w .25 & .49 may be called “moderate”

Values b/w .50 & .74 may be called “moderately strong”

Values b/w .75 and .99 may be called “strong”

A value of 1.00 is called “perfect”

Describing strength of relationships with positive or negative values

What is true in the positive is true in the negative

Ex: values b/w .75 & .99 are “very strong” & values b/w -.75 & -.99 are “very strong” though it is an inverse relationship

Correlation Coefficients &Scatterplots

Scatterplots used to visually show trend of data

Tells us

If relationship indicated

Kind of relationship

Outliers – cases differing from general trend

Graph may indicate direction, strength, and/or relationship of two variables

NOTE

It is ESSENTIAL to plot a scatter plot before conducting correlation analysis

If no relationship found in scatter plot,

No need to conduct correlation

When to Use Pearson r

Use Pearson r when:

Looking at relationship b/w 2 scale variables

Interval or ratio measurements

Data not highly skewed

Distribution of scores is approximately symmetrical

Relationship b/w variables is linear

When to Use Spearman’s rho

Use Spearman’s rho when:

One or both variables are ordinal

Ex: college degree, weight, or height given ranking order (i.e. 1 = lightest, 2 = middle, 3 = heaviest)

One or both sets of data are highly skewed

Distributions are not symmetrical

Relationship is not curvilinear

As determined in examination of scatter plot

Spearman Rank Order Correlation

This correlation coefficient is simply the Pearson r calculated on the rankings of the X and Y variables.

Because ranks of N objects are the integers from 1 to N, the sums and sums of squares are known (provided there are no ties).

Spearman Rank Order Correlation

Spearman Rank Order Correlation

Spearman Rank Order Correlation

Since we know the sum of the scores and the sum of their squares, we automatically know the variance of the integers from 1 to N.

Spearman Rank Order Correlation

Suppose we compute it with N in the denominator instead of

Spearman Rank Order Correlation

Example

Different Scales, Different Measures of Association

Used to describe the linear

    relationship between two variables

    that are both interval or ratio variables

The symbol for Pearson’s correlation

    coefficient is r

The underlying principle of r is that it

    compares how consistently each Y

    value is paired with each X value in a

    linear fashion

The Pearson Correlation formula

         

             degree to which X and Y vary together

r = ---------------------------------------------------

         degree to which X and Y vary separately

      

               Co-variability of X and Y

 = -----------------------------------------

           variability of X and Y separately

          ∑XY-(∑X)(∑Y)/N

      r = -----------------------------------------

         √ (∑X*2 –(∑X) *2/N) (∑Y*2 –(∑Y) *2/N)

            Degree of freedom=N-2

Sum of Product Deviations

We have used the sum of

   squares or SS to measure the amount

   of variation or variability for a single

   variable

The sum of products or SP provides a

   parallel procedure for measuring the

   amount of co variation or co variability

   between two variables

Definitional Formula

SS =Σ (X- x)(X -x)

     or =Σ (Y -y)(Y -y)

Note :

              SP =Σ (X -x)(Y- y)

example

X Y XY

1 3 3

2 6 12

4 4 16

5 7 35

ΣX=12

ΣY=20

ΣXY=66

Substituting:

SP = 66 - 12(20)/4

= 66 - 60

= 6

Calculation of Pearson’s
Correlation Coefficient

Pearson’s correlation coefficient is a

   ratio comparing the co variability of X

  and Y (the numerator) with the

  variability of X and Y separately (the

  denominator)

SP measures the co variability of X and Y  The variability of X and Y is measured by calculating the SS for X and Y scores separately

Pearson correlation coefficient

r = SP / √ SS X SS Y

example

X    Y  X-X  Y-Y  (X-X)(Y-Y)  (X-X)2  (Y-Y)2

    0    1   -6       -1       +6            36                 1

   10   3   +4      +1      +4            16           1

4     1   -2       -1       +2             4          1

8           2   +2        0         0             4            0

8     3   +2      +1        +2            4            1

SP = 6+4+2+0+2 = 14

SSX = 36+16+4+4+4 = 64

SSY = 1+1+1+0+1

r = SP / √ SS X SS Y

r=  14/√ 64 * 4

       

 14 ÷ 16

= + 0.875

Inferential statistics

Regression

Regression.  The best fit line of prediction.

Using a correlation (relationship between variables) to predict one variable from knowing the score on the other variable

Usually a linear regression (finding the best fitting straight line for the data)

Best illustrated in a scatter plot with the regression line also plotted

The scatter plot

In correlation data, it is sometimes useful to

regard one variable as an independent variable  and the other as a dependent variable.

In these circumstances, a linear relationship

between two variables X and Y can be

expressed by the equation Y=bX + a

Where Y is the dependent variable, X the

independent variable and b and a are

constants

In the general linear equation the value of

b is called the slope

The slope determines how much the Y

variable will change when X is increased

by one point

The value of a in the general equation is

called the Y-intercept(cutting the graph)

It determines the value of Y when X=0

A regression is a statistical method for studying the relationship between a single dependent variable and one or more independent variables.

In its simplest form a regression specifies a linear relationship between the dependent and independent variables.

            Yi = b0 + b1 X1i + b2 X2i + ei

for a given set of observations

In the social sciences, a regression is generally used to represent a causal process.

Y represents the dependent variable

B0 is the intercept (it represents the predicted value of Y if X1 and X2 equal zero.)

X1 and X2 are the independent variables (also called predictors or regressors)

b1 and b2 are called the regression coefficients and provide a measure of the effect of the independent variables on Y (they measure the slope of the line)

e is the stuff not explained by the causal model.

Why use regression?

Regression is used as a way of testing hypotheses about causal relationships.

Specifically, we have hypotheses about whether the independent variables have a positive or a negative effect on the dependent variable.

Just like in our hypothesis tests about variable means, we also would like to be able to judge how confident we are in our inferences.

Standard Error of Estimate

A regression equation, by itself,

allows you to make predictions, but it

does not provide any information

about the accuracy of the predictions

The standard error of estimate gives a

measure of the standard distance

between a regression line and the

actual data points

To calculate the standard error of estimate

Find a sum of squared deviations (SS)

Each deviation will measure the distance

between the actual Y value (data) and the

predicted Ŷ value (regression line)

This sum of squares is commonly called

SSerror

Definition of Standard Error

The standard deviation of the sampling distribution is the standard error.  For the mean, it indicates the average distance of the statistic from the parameter.

Example of Height

Raw Data vs. Sampling Distribution

Formula: Standard Error of Mean

To compute the SEM, use:

For our Example:

Standard Error (SE)

It has become popular recently

Researchers often misunderstand and mis- use SE

Variability of observations is SD while variability of 2 or more sample means is SE

Therefore, often called “Standard error of the means” and SD of a set of observations or a population

Covariance

When two variables covary in opposite directions, as smoking and lung capacity do, values tend to be on opposite sides of the group mean.  That is, when smoking is above its group mean, lung capacity tends to be below its group mean.

Consequently, by averaging the product of deviation scores, we can obtain a measure of how the variables vary together.

The Sample Covariance

Instead of averaging by dividing by N, we divide by          .    The resulting formula is

Calculating Covariance

Calculating Covariance

So we obtain

What is Analysis of Variance?

ANOVA is an inferential test designed for use with 3 or more data sets

t-tests are just a form of ANOVA for 2 groups

ANOVA only interested in establishing the existence of a statistical differences, not their direction.

Based upon an F value (R. A. Fisher) which reflects the ratio between systematic and random/error variance…

Procedure for computing 1-way ANOVA for independent samples

Step 1: Complete the table

                        i.e.

                                    -square each raw score

                                    -total the raw scores for each group

                                    -total the squared scores for each group.

Step 2: Calculate the Grand Total correction factor

                       

                        GT =

                             

                              =

                             

Step 3: Compute total Sum of Squares

                       

SStotal= åX2 - GT

                             

                              = (åXA2+XB2+XC2) - GT

                          

Step 4: Compute between groups Sum of Squares

                       

SSbet=                 - GT

                             

                              =             +                +               - GT

                       

Step 5: Compute within groups Sum of Squares

                       

SSwit= SStotal - SSbet

                             

                       

Step 6: Determine the d.f for each sum of squares

                       

dftotal= (N - 1)

   

            dfbet= (k - 1)

            dfwit= (N - k)

Step 7/8: Estimate the Variances & Compute F

                       

                                          =

                         

                                            =

Step 9: Consult F distribution table

-d1 is your df for the numerator (i.e. systematic variance)

-d2 is your df for the                                                        denominator                                                                   (i.e. error variance)                            

Statistical Decision Process

Type I error – rejecting a true null hypothesis. (treatment has an effect when in fact the treatment has no effect)

Alpha level for a hypothesis test is the probability that the test will lead to a Type I error

Alpha and Probability Values

The level of significance that is selected prior to data collection for accepting or rejecting a null hypothesis is called alpha. The level of significance actually obtained after the data have been collected and analyzed is called the probability value, and is indicated by the symbol p.

Inferential Statistics

Level of significance. The second determinant of statistical power is the p value at which the null hypothesis is to be rejected. Statistical power can be increased by lowering the level of significance needed to reject the null hypothesis.

Error Types

Example - Efficacy Test for New drug

Type I error - Concluding that the new drug is better than the standard (HA) when in fact it is no better (H0). Ineffective drug is deemed better.

Type II error - Failing to conclude that the new drug is better (HA) when in fact it is. Effective drug is deemed to be no better.

Non- parametric                      statistics

Non-parametric methods

So far we assumed that our samples were drawn from normally distributed populations.

techniques that do not make that assumption are called distribution-free or nonparametric tests.

In situations where the normal assumption is appropriate, nonparametric tests are less efficient than traditional parametric methods.

Nonparametric tests frequently make use only of the order of the observations and not the actual values.

Usually do not state hypotheses in terms of a specific parameter

They make vary few assumptions about the population distribution- distribution-free tests.

Suited for data measured in ordinal and nominal scales

Not as sensitive as parametric tests; more likely to fail in detecting a real difference between two treatments

Statistical analysis that attempts to explain the population parameter using a sample without making assumption about the frequency distribution of the assessed variable

In other words, the variable being assessed is distribution-free

Types of nonparametric tests

Chi-square statistic tests for Goodness of Fit (how well the obtained sample proportions fit the population proportions specified by the null hypothesis

Test for independence – tests whether or not there is a relationship between two variables

Non-Parametric Methods

Spearman Rho Rank Order Correlation Coefficient

To calculate the Spearman rho:

Rank the observations on each variable from lowest to highest.

Tied observations are assigned the average of the ranks.

The difference between the ranks on the X and Y variables  are summed and squared:

rrho = 1 – [(6åD2)/ n (n2 – 1)

Is there a relationship between the number of cigarettes smoked and severity of illness?

The null and alternative hypotheses are:

HO: There is no relationship between the number of cigarettes smoked and severity of illness

HA: This is a relationship between the number of cigarettes smoked and severity of illness

a  = .05

rrho     = 1 – [(6åD2)/ n (n2 – 1)]

                                    = 1 – [6(24) / 8(64-1)]

                                    = .71

tcalc = 2.49

tcrit = 2.447, df = 6, p = .05

Since the calculated t is > the critical value of t, we reject the null hypothesis and conclude that there is a statistically significant positive relationship between the number of cigarettes smoked and severity of illness

Use:A non-parametric procedure that we can use to assess the relationship between variables is the Spearman rho.

Goodness  of Fit

The chi-square test is a “goodness of fit” test

 it answers the question of how well do experimental data fit expectations.

Example

As an example, you count F2 offspring, and get  290 purple and 110 white flowers.  This is a total of 400 (290 + 110) offspring.

We expect a 3/4 : 1/4 ratio.  We need to calculate the expected numbers (you MUST use the numbers of offspring, NOT the proportion!!!); this is done by multiplying the total offspring by the expected proportions.  This we expect 400 * 3/4 = 300 purple, and 400 * 1/4 = 100 white. 

Thus, for purple, obs = 290 and exp = 300.  For white, obs = 110 and exp = 100.

Chi square formula

Now it's just a matter of plugging into the formula: 

         2 = (290 - 300)2 / 300 + (110 - 100)2 / 100

              =  (-10)2 / 300 + (10)2 / 100

              =  100 / 300 + 100 / 100

              = 0.333 + 1.000

              = 1.333.  

This is our chi-square value

State H0                          H0 :   120

State H1                       H1  : ¹

Choose                      = 0.05

Choose n                     n = 100

Choose Test:    Z, t, X2 Test (or p Value)       

  Compute Test Statistic (or compute P value)

  Search for Critical Value

  Make Statistical Decision rule

  Express Decision              

Steps in Test of Hypothesis

Determine the appropriate test

Establish the level of significance:α

Formulate the statistical hypothesis

Calculate the test statistic

Determine the degree of freedom

Compare computed test statistic against a tabled/critical value

1.  Determine Appropriate Test

Chi Square is used when both variables are measured on a nominal scale.

It can be applied to interval or ratio data that have been categorized into a small number of groups.

It assumes that the observations are randomly sampled from the population.

All observations are independent (an individual can appear only once in a table and there are no overlapping categories).

It does not make any assumptions about the shape of the distribution nor about the homogeneity of variances.

2. Establish Level of Significance

α is a predetermined value

The convention

α = .05

α = .01

α = .001

3. Determine The Hypothesis:
Whether There is an Association or Not

Ho : The two variables are independent

Ha : The two variables are associated

4. Calculating Test Statistics

5. Determine Degrees of Freedom

df = (R-1)(C-1)

6. Compare computed test statistic against a tabled/critical value

The computed value of the Pearson chi- square statistic is compared with the critical value to determine if the computed value is improbable

The critical tabled values are based on sampling distributions of the Pearson chi-square statistic

If calculated c2 is greater than c2 table value, reject  Ho

Example

Suppose a researcher is interested in voting preferences on gun control issues.

A questionnaire was developed and sent to a random sample of 90 voters.

The researcher also collects information about the political party membership of the sample of 90 respondents.

Bivariate Frequency Table or Contingency Table

1.  Determine Appropriate Test

Party Membership ( 2 levels) and Nominal

Voting Preference ( 3 levels) and Nominal

2. Establish Level of Significance

Alpha of .05

3. Determine The Hypothesis

Ho : There is no difference between D & R in their opinion on gun control issue.

Ha : There is an association between responses to the gun control survey and the party membership in the population.

4. Calculating Test Statistics

5. Determine Degrees of Freedom

df = (R-1)(C-1) =
(2-1)(3-1) = 2

Critical Chi-Square

Critical values for chi-square are found on tables, sorted by degrees of freedom and probability levels.  Be sure to use p = 0.05.

If your calculated chi-square value is greater than the critical value from the table, you “reject the null hypothesis”.

If your chi-square value is less than the critical value, you “fail to reject” the null hypothesis (that is, you accept that your genetic theory about the expected ratio is correct).

Chi-Square Table

6. Compare computed test statistic against a tabled/critical value

α = 0.05

df = 2

Critical tabled value = 5.991

Test statistic, 11.03, exceeds critical value

Null hypothesis is rejected

Democrats & Republicans differ significantly in their opinions on gun control issues

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