Effects of variables on type II error

δ is the difference between the two means. As the value of δ increases the two curves will separate and the power of the study will increase. As δ gets smaller, the curves will overlap and the power of the study will decrease.

α is usually set at 5%. Sometimes α is set at 1%, moving away from the centre of the distribution,and this will reduce the power of the study. In this simulation, α has a maximum value of 10%, to show that this increases the power of the study, but it would not be set at that level in a real situation.

n is the number or size of the samples. This affects the standard error. When n is low the sample size is small, the curves are flatter and overlap more so, assuming α is held constant, this reduces the power of the study. As n increases the standard error decreases and the curves have narrower distributions, hence they overlap less.

σ is the standard deviation of the measurement you're interested in. Generally speaking a researcher has no control over this, but sometimes you can design your study so you reduce σ for example there are some well defined protocols for measuring blood pressure that reduce measurement error. Or you can restrict your study to a more homogeneous population, for example blood pressure of men aged 40 - 45. When σ is reduced the effect is to make the width of the distributions smaller and hence the curves overlap less. So this increases the power assuming you hold α constant. Conversely, as σ increases, the width of the distributions get bigger and the curves overlap more so the power of the study is reduced.