Longman Dictionary of

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hypothesis formation
ANALYTIC INDUCTION). If for research purposes the speculation is translated
into a statement that can be tested by quantitative methods in
research, the statement is known as a statistical hypothesis, stated with
reference to population PARAMETERs (e.g. population mean) and takes the
form of two opposing but related hypotheses: a null hypothesis, symbolized
by H 0 , and an alternative hypothesis, symbolized by H a or H 1 , that
are mutually exclusive and exhaustive. A null hypothesis is a statement
that “No difference exists between groups A and B” or “There is no correlation
between variables A and B”, whereas the alternative hypothesis
is an opposite statement that “The mean for group A is higher than that
for group B” or “There is a positive correlation between variables A and
B”. The statistical analysis of research results is frequently designed to
determine whether or not a null hypothesis should be rejected, thus providing
support for an alternative hypothesis.
see also HYPOTHESIS TESTING
hypothesis formation n
(in language learning) the formation of ideas (“hypotheses”) by a learner
about the language he or she is learning. These hypotheses may be conscious
or unconscious. Most people would agree that at least some of
these ideas come from the language we see and hear around us, but scholars
holding the INNATIST HYPOTHESIS claim that our most important
and basic ideas about language in general are present at birth.
hypothesis testing n
a procedure to test a statistical hypothesis. A five-step version of hypothesis
testing proceeds as follows:
1 State a null hypothesis (H 0 ) and an alternative hypothesis (H a ).
2 Set a level of statistical significance () (see ALPHA).
3 Select and calculate an appropriate test statistic, a numerical value calculated
from the data sampled from a population and used to determine
whether or not H 0 should be rejected, which results in a
calculated value.
4 Compare the sample evidence from Step 3 against a criterion (i.e. a calculated
value against a critical value or a p-value against ).
5 Make a decision regarding the null hypothesis (i.e. either to reject H 0 in
favour of H a or fail to reject H 0 ).
see also STATISTICAL SIGNIFICANCE
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