The Null and Alternative Hypotheses

The most backwards way of researching in the social sciences but basically the Null and Alternative Hypothesis is defined as…

• Null (H0) — the uninteresting hypothesis (you would try and veer away from this hypothesis when researching although you may have to come to this conclusion if the results don’t turn out right).
• Alternative (H1) — the hypothesis we are currently researching and we try to keep this hypothesis to be true.

In order to set your two hypotheses you must have an Alpha Level (can be .05 or .01 most commonly used).

And in order to fully understand this you must view the Normal Distribution table.

An example using the Null and Alternative would be.

• The people who use this new cancer drug have a higher rate of decreasing cancer and getting better than supposedly the control drug/placebo.

Your Research Hypothesis will be:  This new cancer drug cures patients better than the controlled drug.

Your Statistical Hypothesis will be:  The mean of the patients’ cure of cancer with this new drug is higher than the average cure rate of cancer.

The Null (H0) — The new cancer drug does not work and that we should continue using the current drug.

The Alternative (H1) — The new cancer drug does increase curable rates than the controlled drug

Alpha level — .05

If p-value is < .05 — reject the null

If p-value is > .05 — fail to reject the null

*I hope I phrased this concept correctly, it’s a little confusing and this is from my Statistics for the Behavioral Sciences Class which I do not like and may end up receiving a C (my goal) since this is a very hard course*

Although I do find the Null to be quite interesting, but it’s hard to grasp the concept at first. And that’s why I’m going to try and avoid statistics as much as possible, well except when reading journal articles but I won’t be doing the math

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