Question

revisited. Exercises 7 and 21 describe an experiment investigating a dietary approach to treating bipolar disorder. Researchers randomly assigned 30 subjects to two treatment groups, one group taking a high dose of omega-3 fats and the other a placebo. a) Why was it important to randomize in assigning the subjects to the groups? b) What would be the advantages and disadvantages of using 100 subjects instead of 30 ?

Short answer

Randomization prevents bias; using 100 subjects increases reliability but also costs more.

Step-by-step solution

Understanding Randomization

Randomization in a scientific study ensures that each participant has an equal chance of being placed in any group. This process helps eliminate selection bias, balances known and unknown factors across groups, and supports the reliability and validity of the results.

Importance of Randomization

Randomizing subjects is essential as it ensures that the two groups (omega-3 and placebo) are comparable at the start of the experiment. This way, any observed differences in outcomes can be attributed to the treatment rather than pre-existing differences between groups.

Evaluate Advantages of Increasing Sample Size

Using 100 subjects instead of 30 could offer several advantages: increased statistical power, which enhances the ability to detect a true effect of the treatment if one exists, and provides more precise estimates of the treatment effect. Larger sample sizes can yield results that are more generalizable to the broader population.

Assessing Disadvantages of Increasing Sample Size

Potential disadvantages of using 100 subjects include increased costs and logistical complexity. Larger sample sizes require more resources in terms of time, money, and manpower to recruit, monitor, and collect data. There's also a risk of unnecessarily overcomplicating the experiment if the original sample size is sufficiently powered.

Key concepts

Sample Size

When conducting an experiment, the sample size refers to the number of subjects or observations included in the study. In our context with 30 versus 100 subjects, the sample size can dramatically influence the outcome and interpretation of the research findings. Larger sample sizes, like 100, generally provide more reliable and valid results. They can detect smaller effects of the treatment due to increased statistical power, reducing the chance of Type II errors.

This means a larger sample size increases the likelihood of finding a significant treatment effect if one actually exists. Moreover, larger samples are usually more representative of the population as a whole, making results more generalizable. However, they can also be harder to manage and cost more in terms of resources. More subjects mean more data collection, management, and analysis costs.

Conversely, smaller sample sizes, such as 30, might be easier to handle financially and logistically but risk missing significant effects due to insufficient power. They might not represent the entire population well, which could skew the findings if the sample is not selected carefully.

Experimental Design

Experimental design is the fundamental blueprint of the study that ensures its validity and reliability. It includes the process of structuring the experiment so that valid and reliable conclusions can be drawn. In the bipolar disorder study, a randomized control trial is implemented by assigning subjects randomly to either the treatment (omega-3) or placebo group. This design aims at minimizing the biases that might affect the results.

Key elements of experimental design include randomization, control, and replication. Randomization, like the one used in this experiment, helps in distributing any potential biases and confounding variables evenly among groups.

This makes sure differences in the outcomes between groups are likely due to the treatment rather than extraneous factors. Control is achieved by having a placebo group, which supplies a baseline for measuring the effect of the treatment. Additionally, replication or having enough participants helps ensure that results are consistent and statistically valid.

Statistical Power

Statistical power is a concept that reflects the probability of a study to detect an effect if there is one. It depends on several factors, including sample size, effect size, and significance level. In simple terms, greater statistical power means a higher chance of finding a true effect of a treatment when it actually exists.

A study with higher statistical power is beneficial as it reduces the likelihood of Type II errors, where a study fails to recognize an effect that's truly there. Using a larger sample size, such as 100 subjects instead of 30, typically enhances statistical power. This is because more data points provide a clearer picture of the effect, reducing the noise and uncertainty present with smaller samples.

Designing studies with adequate statistical power ensures that the research findings are reliable and can be trusted. It also implies that the opportunity to discover meaningful differences between the treatment and placebo groups is maximized.