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Simply, a mere assumption to be proved or disproved
But for a researcher, hypothesis is a formal question that he intends to resolve
Definition: “A proposition or a set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena asserted merely as a provisional conjecture to guide some investigation or accepted as highly probable in the light of established facts”
Often hypothesis is a predictive statement capable of being tested by scientific methods, that relates an independent variable to some dependent variable
Ex: students who receive counseling will show greater increase in creativity than students not receiving counseling; or Car A is performing as well as Car B
In sum, hypothesis is a proposition which can be put to test to determine its validity
If we are to compare Method A with Method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as the Null Hypothesis
As against the above, we may think that the Method A is superior or that the Method B is inferior, we are then stating what is termed as Alternative Hypothesis
In the context of hypothesis-testing, the level of significance is an important concept
It is always some percentage (usually 5%)
This implies that the null hypothesis will be rejected, when the sampling result (observed evidence) has less than 0.05 probability of occurring if the null hypothesis is true
That is, the 5% level of significance means that the researcher is willing to take as much as a 5% risk of rejecting the null hypothesis when it happens to be true
Type I Error – we may reject the null hypothesis when it is true; and
Type II Error – we may accept the null hypothesis when in fact the null hypothesis is not true
That is, Type I error means rejection of the hypothesis which should have been accepted and Type II error means accepting the hypothesis which should have been rejected
Consists in making a formal statement of the null hypothesis and also the alternative hypothesis
Ex: The average score in an aptitude test at the national level is 80. To evaluate a state’s education system, the average score of 100 of the state’s students selected on random basis is 75. The state wants to know if there is a significant difference between the state’s scores and the national scores.
Hypothesis may be stated as follows:
Null hypothesis: population mean = 80
Alternative hypothesis: population mean is not equal to 80
The next step is to calculate the probability that the sample result would diverge as it has from expectations, if the null hypothesis were in fact true
The next step is to compare the probability thus calculated with the specified value (the significance level)
If the calculated probability is equal to or smaller than the significance level, then reject the null hypothesis (i.e. accept the alternative hypothesis); but if the calculated probability is greater, then accept the null hypothesis
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