Research hypothesis: States in plain language that there’s no relationship between the 2 variables or there’s no difference between the 2 groups being studied. Statistical hypothesis: States the predicted outcome of statistical analysis through a mathematical equation related to the statistical method you’re using.
Null hypothesis H0{\displaystyle H_{0}}: High school sex education has no effect on teen pregnancy rates. Alternative hypothesis Ha{\displaystyle H_{a}}: High school sex education lowers teen pregnancy rates.
Null hypothesis H0{\displaystyle H_{0}}: There is no relationship between social media use and the attention spans of children under 16. Alternative hypothesis Ha{\displaystyle H_{a}}: There is a negative correlation between social media use and attention span in children under 16.
Null hypothesis H0{\displaystyle H_{0}}: There is no relationship between gender and the choice to go vegan. Alternative hypothesis Ha{\displaystyle H_{a}}: Women are more likely than men to go vegan.
Null hypothesis H0{\displaystyle H_{0}}: There is no relationship between age and the ability to read highway signs at a distance. Alternative hypothesis Ha{\displaystyle H_{a}}: People’s ability to read highway signs at a distance decreases as they get older.
For example, your alternative hypothesis could state a positive correlation between 2 variables while your null hypothesis states there’s no relationship. If there’s a negative correlation, then both hypotheses are false.
You need additional data or evidence to show that your alternative hypothesis is correct—proving the null hypothesis false is just the first step. In smaller studies, sometimes it’s enough to show that there’s some relationship and your hypothesis could be correct—you can leave the additional proof as an open question for other researchers to tackle.
Group means: Compare the mean of the variable in your sample with the mean of the variable in the general population. [9] X Research source Group proportions: Compare the proportion of the variable in your sample with the proportion of the variable in the general population. [10] X Research source Correlation: Correlation analysis looks at the relationship between 2 variables—specifically, whether they tend to happen together. [11] X Research source Regression: Regression analysis reveals the correlation between 2 variables while also controlling for the effect of other, interrelated variables. [12] X Research source
Research null hypothesis: There is no difference in the mean [dependent variable] between [group 1] and [group 2]. Statistical null hypothesis: μ1=μ2{\displaystyle \mu _{1}=\mu _{2}} or μ1+μ2=0{\displaystyle \mu _{1}+\mu _{2}=0}
Research null hypothesis: The proportion of [dependent variable] in [group 1] and [group 2] is the same. Statistical null hypothesis: p1=p2{\displaystyle p_{1}=p_{2}}
Research null hypothesis: There is no correlation between [independent variable] and [dependent variable] in the population. Statistical null hypothesis: ρ=0{\displaystyle \rho =0}
Research null hypothesis: There is no relationship between [independent variable] and [dependent variable] in the population. Statistical null hypothesis: β=0{\displaystyle \beta =0}
