1 pptx hypothesis testing

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ByDr. Mojgan Afshari

Hypothesis Testing


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Descriptive statistics

Descriptive statistics are used to describe andsummarize the properties of the mass of data

collected from the respondents.

Measures of central tendency, such as meanand median, and dispersion, such as range andstandard deviation, are the main descriptivestatistics. Displays of data, such as histograms

and box-plots, are also considered techniquesof descriptive statistics.

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Inferential statistics

Inferential statistics are used to infer theproperties of the population from the

properties of the sample.

T-test, ANOVA, Chi-square, Spearman Rho,

Pearson PM Correlation, and Simple Linear

Regression are examples of inferentialstatistics.

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Population & Sample

The population must be defined explicitlybefore a sample is taken.

The bigger the sample, the more likely it is to

reflect the whole population.


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The basic requirement of inferential statistics isthe sample to be selected must be randomlychosen from the population. (A random sample

is one in which each individual in theprescribed population has an equal chance to

be included in the sample). Consequently,

findings from the sample can be generalized tothe population.

Sampling will determine how much and howwell the researcher may generalize his or her

findings. A bad sample may well render

findings meaningless.5


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THE MAJOR STATISTICAL

TECHNIQUES

Techniques used to explore differencesamong groups

Techniques used to explore relationshipsamong variables

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What factors determine the most

appropriate statistical techniques

1. Research objectives/ questions / purpose of

your study2. Measurement scales you used in your

research instrument

3. Research design of your studies4. Nature of your data ( meeting normality and /

or equality of variance assumptions for

parametric test)[Please see chapter 10 (Choosing the right statistic), Pallant, Julie, F.

(2007). SPSS Survival Manual: A Step by Step Guide to Data Analysis

Using SPSS for Windows.]7


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Suggested methods for data analysis based on

measurement scale and number of dependentand independent variables

Ordinal Ordinal


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Hypothesis Testing

Hypothesis refers to educated guess or assumption tobe tested

Hypothesis is formulated following the review ofrelated literature and prior to the execution of the study

Setting up and testing hypotheses is an essential partof statistical inference

Types of hypotheses:

1.Research hypothesis HA

2.Null hypothesis H0


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Characteristics of a Good Hypothesis

A major characteristic of a good research hypothesisis that it is consistent with previous research

A good hypothesis is a tentative, reasonableexplanation for the occurrence of certain behaviors,phenomena, or events

A good hypothesis states as clearly and concisely aspossible the expected relationship or difference

between two variables

A well-stated and defined hypothesis must be

testable


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Research Hypothesis HA

Also known as alternative hypothesis

A statement of what a statistical hypothesis testis set up to establish

In an experiment, the alternative hypothesismight be that the new teaching method has a

different effect, on average, compared to that ofthe current method

Or the alternative hypothesis might also be thatthe new method is better, on average, than the

current method


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Critical/

Rejection Region

Critical/

Rejection Region

Critical valueCritical value

Critical value is the value of the teststatistic that is needed to reject the

null hypothesis

The set of values of the teststatistic beyond the critical value is

called the critical or rejection region

Reject the null hypothesis if the value of the test statistic that fall beyond thecritical value and within the critical region.


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Reject H0: Calculated statistic > Critical value

Fail to reject H0: calculated statistic

Criticalvalue

Reject H0: Sig-value <

Fail to reject H0: Sig-value


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Assignment

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1. Describe the importance of descriptive and

inferential statistics in research?2. Which of the following is the highest type 1

error ? Explain why?

a. = .100b. = .01

c. =.001

d. =.0001

3. A researcher tested a hypothesis at the 0.05

significance level. Explain the meaning of thisstatement.

4. Explain factors that should be considered

before choosing a specific statisticalprocedure.28

1 pptx hypothesis testing

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