A research hypothesis is a statement created by researchers to speculate on the outcome of an experiment. Hypotheses are generated through inductive reasoning from observations and must be testable, falsifiable, and realistic. There are two types of errors in hypothesis testing: type I errors which incorrectly reject a true null hypothesis, and type II errors which fail to reject a false null hypothesis. Examples of hypotheses and errors are given for building inspections and the effects of fluoride in toothpaste.

2. HYPOTHESIS
A research hypothesis (H1) is the statement created
by researchers when they speculate upon the
outcome of a research or experiment. ( Lyndsay T
Wilson)
The hypothesis is generated via a number of means,
but is usually the result of a process of inductive
reasoning where observations lead to the formation
of a theory.

3.  Scientists then use a large battery of deductive
methods to arrive at a hypothesis that
is testable, falsifiable and realistic.

4. FEATURES
 Power of prediction
 Clarity
 Simplicity
 Testibility
 Relevant to problems
 Specific
 Fruitful for new discoveries
 Consistancy and harmony.
 Closest to observable things.

5. TYPES OF ERROR
 Type-I error :- A Type I error (sometimes called a
Type 1 error), is the incorrect rejection of a true null
hypothesis. The alpha symbol, α, is usually used to
denote a Type I error.
 An α of 0.05 indicates that you are willing to accept a
5% chance that you are wrong when you reject the
null hypothesis.
 To lower this risk, you must use a lower value for α.
However, using a lower value for alpha means that
you will be less likely to detect a true difference if one
really exists.

6. EXAMPLES
 Let’s say that our null hypothesis is that there is “no
wolf present.” A type I error (or false positive) would
be “crying wolf” when there is no wolf present.
 That is, the actual condition was that there was no
wolf present; however, the shepherd wrongly
indicated there was a wolf present by calling “Wolf!
Wolf!”
 This is a type I error or false positive error.

7. Building inspection
 An inspector has to choose between certifying a
building as safe or saying that the building is not safe.
 There are two hypotheses: Building is safe or not
safe.
 H0 : Building is not safe
Ha : Building is safe

8. TYPE II ERROR
 A Type II error (sometimes called a Type 2 error) is the
failure to reject a false null hypothesis.
 The probability of a type II error is denoted by the beta
symbol β.
 A type II error is a statistical term used within the
context of hypothesis testing that describes the error
that occurs when one accepts a null hypothesis that is
actually false.
 The error rejects the alternative hypothesis, even
though it does not occur due to chance. A type II error
fails to reject, or accepts, the null hypothesis, although
the alternative hypothesis is the true state of nature.

9. EXAMPLE
 Hypothesis: "Adding fluoride to toothpaste protects
against cavities."
 Null hypothesis (H0): "Adding fluoride to toothpaste has
no effect on cavities."
 This null hypothesis is tested against experimental data
with a view to nullifying it with evidence to the contrary.
 A type II error occurs when failing to detect an effect
(adding fluoride to toothpaste protects against cavities)
that is present. The null hypothesis is false (i.e., adding
fluoride is actually effective against cavities), but the
experimental data is such that the null hypothesis cannot
be rejected.

10. HYPOTHESIS STATEMENT EXAMPLE

11. Thank you