Research HypothesisResearch Hypothesis (H1) is the specific

testable prediction about the independent and dependent variable in your study.

An example would be"Children who are exposed to regular singing

of the alphabet will show greater recognition of letters than children who are exposed to regular pronouncing of the alphabet"

Notice the IV is specified (singing compared to pronouncing) and the DV is specified (recognition of letters is what will be measured).

Null Hypothesis or Alternate HypothesisThe null Hypothesis (H0) is a hypothesis

which the researcher tries to disprove, reject or nullify.

An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.

ExampleWe want to Study the smoking pattern in a

community in relation to gender differentials.The following Hypothesis could be constructed

1.There is no significant difference in the proportion of male and female smokers in the study population.

Hypothesis Of difference A Hypothesis in which a researcher specify

that there will be a difference but does not specify its magnitude (Quantity) is called hypothesis of difference

ExampleA greater proportion of females than males

are smokers in the study population.

Hypothesis of Point Prevalence When the researcher specify almost exact

prevalence of a situation, this type of hypothesis is called Hypothesis of Point prevalence.

ExampleA total of 60 percent of females and 30 percent males in the study population are smokers.

Hypothesis of AssociationThis type of hypothesis specify extent of a

relationship in terms of prevalence of a phenomenon in different population subgroups .

ExampleThere are twice as many female smokers as male smokers in the study population

Alternate Hypothesis

Research Hypothesis

Types of hypothesis

Null Hypothesis

Association

PointPrevalen

ce

Hypothesis of

Difference

Hypothesis of No

difference

There are two types of hypotheses: descriptive and directional.

1.Descriptive HypothesisDescriptive hypotheses ask a specific question regarding some phenomenon. For example, we might want to study this research question: what are the social and economic characteristics of patients who have high blood pressure?

A descriptive hypotheses that would test a part of the above research question is: what is the distribution of hypertensive patients by income level?

Descriptive hypotheses are always phrased in the form of a question regarding some aspect of the research question.

Usually a descriptive hypothesis does not include an active independent variable. When we use an independent variable, a directional hypothesis is usually needed.

Directional HypothesisSpecify the outcome of the experimentDirectional hypothesis are those where one

can predict the direction (effect of one variable on the other as 'Positive' or 'Negative')

for e.g: Girls perform better than boys ( 'better than' shows the direction predicted )

Non Directional HypothesisDo not predict the exact directional outcome of

an experiment, but only that the groups we are testing will differ.

for e.g. There will be a difference in the performance of girls & boys (Not defining what kind of difference)

Characteristics of the Research HypothesisTypes of StatementsSynthetic Statements

Are those statements that can be either true of false (e.g. “Abused children have lower self-esteem

Analytic StatementsAre those statements that are always true (e.g. I

am making an “A” or I am not making an “A”).

Contradictory StatementsAre those statements that are always false

(e.g. I am making an “A” and I am not making an “A.

Which type of statement is best suited for use in our research hypothesis?

General Implication Form You must be able to state (or restate ) the

research hypothesis in general implication (“if…then”) form

The “if” portion of such statements refers to the independent variable manipulation(s) that we are going to make, whereas the “then” portion of the statement refers to the dependent variable changes we expect to observe.

Principle of FalsifiabilityWhen an experimental hypothesis is stated in

general implication form, it is possible that a result is true (supported by the results of the study) or false (not supported by the results of the study

Types of ReasoningInductive Logic

Involves reasoning from specific cases to general principles. Inductive logic is the process that is involved in the construction of theories.

HYPOTHESIS TESTINGA statistical hypothesis test is a method of

making decisions using data, whether from a controlled experiment or an observational study .

For example, you might have come up with a measurable hypothesis that children have a higher IQ if they eat oily fish for a period of time.

Your alternative hypothesis, H1 would be“Children who eat oily fish for six months will

show a higher IQ increase than children who have not.”

Therefore, your null hypothesis, H0 would be“Children who eat oily fish for six months do not

show a higher IQ increase than children who do not.”

In other words, with the experiment design, you will be measuring whether the IQ increase of children fed oily fish will deviate from the mean, assumed to be the normal condition.

“H0 = No increase. The children will show no increase in mean intelligence.”

From IQ testing of the control group, you find that the control group has a mean IQ of 100 before the experiment and 100 afterwards, or no increase. This is the mean against which the sample group will be tested.

The children fed fish show an increase from 100 to 106. This appears to be an increase, but here is where the statistics enters the hypothesis testing process. You need to test whether the increase is significant or if experimental error could account for the difference.

SIGNIFICANCE TESTSThe tests establish whether there is a

relationship between the variables, or whether pure chance could produce the observed results.

Using an appropriate test, the researcher compares the two means, taking into account the increase, the number of data samples and the relative randomization of the groups. A result showing that the researcher can have confidence in the results allows rejection of the null hypothesis.

ERRORS IN HYPOTHESIS TESTING

A patient might take an HIV test, promising a 99.9% accuracy rate. This means that 1 in every 1000 tests could give a ’false positive,’ informing a patient that they have the virus, when they do not.

Conversely, the test could also show a false negative reading, giving an HIV positive patient the

Conversely, the test could also show a false negative reading, giving an HIV positive patient the all-clear.

TYPE I Error

A Type I error is often referred to as a ’false positive’, and is the process of incorrectly rejecting the null hypothesis in favor of the alternative. In the case above, the null hypothesis refers to the natural state of things, stating that the patient is not HIV positive.The alternative hypothesis states that the patient does carry the virus. A Type I error would indicate that the patient has the virus when they do not, a false rejection of the null.

TYPE II ERRORA Type II error is the opposite of a Type I

error and is the false acceptance of the null hypothesis. A Type II error, also known as a false negative, would imply that the patient is free of HIV when they are not, a dangerous diagnosis.