Knowt
Hypotheses
Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation
Null Hypotheses (H0) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1)– these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but do not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’
All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.
Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.
So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.
Hypothesis testing steps:
set up the null hypothesis, Ho, and the alternative hypothesis, H1
obtain some sample data and summarise these in the test statistic
obtain the null distribution; this is the distribution of the test statistic under the assumption that Ho is true
decide on the significance level for the test; the significance level is the percentage of tests in which Ho would be rejected when it is true and is usually one of 1%, 5% or 10%
calculate the critical values for the significance level, and hence the rejection region for the test; the latter, defined by the former, is the set of extreme values of the test statistic which lead to the rejection of Ho
make one of two possible decisions:
Reject Ho if the test statistic lies in the rejection region
Do not reject Ho if the test statistic does not lie in the rejection region
state the conclusion of the test in non-technical language.
Formulating a null and alternative hypothesis:
You design an experiment to test whether actively smiling can make people feel happier. To begin, you restate your predictions into a null and alternative hypothesis.
Ho: There is no difference in happiness between actively smiling and not smiling.
Ha: Actively smiling leads to more happiness than not smiling.
Then, you decide whether the null hypothesis can be rejected based on your data and the results of a statistical test. Since these decisions are based on probabilities, there is always a risk of making the wrong conclusion.
If your results show statistical significance, that means they are very unlikely to occur if the null hypothesis is true. In this case, you would reject your null hypothesis. But sometimes, this may actually be a Type I error.
If your findings do not show statistical significance, they have a high chance of occurring if the null hypothesis is true. Therefore, you fail to reject your null hypothesis. But sometimes, this may be a Type Il error.
Test statistics and p values:
Every statistical test produces:
A test statistic that indicates how closely your data matches the null hypothesis.
A corresponding p value that tells you the probability of obtaining this result if the null hypothesis is true.
The p value determines statistical significance. An extremely low p value indicates high statistical significance, while a high p value means low or no statistical significance.
Example: Hypothesis testing
To test your hypothesis, you first collect data from two groups. The experimental group actively smiles, while the control group does not. Both groups record happiness ratings on a scale from 1-7.
Next, you perform a t test to see whether actively smiling leads to more happiness. Using the difference in average happiness between the two groups, you calculate:
• a t value (the test statistic) that tells you how much the sample data differs from the null
hypothesis,
• a p value showing the likelihood of finding this result if the null hypothesis is true.
To interpret your results, you will compare your p value to a predetermined significance level.
Example of Null and alternative hypothesis:
You test whether a new drug intervention can alleviate symptoms of an autoimmune disease.
In this case:
The null hypothesis (Ho) is that the new drug has no effect on symptoms of the disease.
The alternative hypothesis (H) is that the drug is effective for alleviating symptoms of the disease.
Hypotheses
Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation
Null Hypotheses (H0) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1)– these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but do not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’
All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.
Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.
So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.
Hypothesis testing steps:
set up the null hypothesis, Ho, and the alternative hypothesis, H1
obtain some sample data and summarise these in the test statistic
obtain the null distribution; this is the distribution of the test statistic under the assumption that Ho is true
decide on the significance level for the test; the significance level is the percentage of tests in which Ho would be rejected when it is true and is usually one of 1%, 5% or 10%
calculate the critical values for the significance level, and hence the rejection region for the test; the latter, defined by the former, is the set of extreme values of the test statistic which lead to the rejection of Ho
make one of two possible decisions:
Reject Ho if the test statistic lies in the rejection region
Do not reject Ho if the test statistic does not lie in the rejection region
state the conclusion of the test in non-technical language.
Formulating a null and alternative hypothesis:
You design an experiment to test whether actively smiling can make people feel happier. To begin, you restate your predictions into a null and alternative hypothesis.
Ho: There is no difference in happiness between actively smiling and not smiling.
Ha: Actively smiling leads to more happiness than not smiling.
Then, you decide whether the null hypothesis can be rejected based on your data and the results of a statistical test. Since these decisions are based on probabilities, there is always a risk of making the wrong conclusion.
If your results show statistical significance, that means they are very unlikely to occur if the null hypothesis is true. In this case, you would reject your null hypothesis. But sometimes, this may actually be a Type I error.
If your findings do not show statistical significance, they have a high chance of occurring if the null hypothesis is true. Therefore, you fail to reject your null hypothesis. But sometimes, this may be a Type Il error.
Test statistics and p values:
Every statistical test produces:
A test statistic that indicates how closely your data matches the null hypothesis.
A corresponding p value that tells you the probability of obtaining this result if the null hypothesis is true.
The p value determines statistical significance. An extremely low p value indicates high statistical significance, while a high p value means low or no statistical significance.
Example: Hypothesis testing
To test your hypothesis, you first collect data from two groups. The experimental group actively smiles, while the control group does not. Both groups record happiness ratings on a scale from 1-7.
Next, you perform a t test to see whether actively smiling leads to more happiness. Using the difference in average happiness between the two groups, you calculate:
• a t value (the test statistic) that tells you how much the sample data differs from the null
hypothesis,
• a p value showing the likelihood of finding this result if the null hypothesis is true.
To interpret your results, you will compare your p value to a predetermined significance level.
Example of Null and alternative hypothesis:
You test whether a new drug intervention can alleviate symptoms of an autoimmune disease.
In this case:
The null hypothesis (Ho) is that the new drug has no effect on symptoms of the disease.
The alternative hypothesis (H) is that the drug is effective for alleviating symptoms of the disease.
