Could anyone help me out with the following problem?

Identify the consequences (i.e. increase, decrease, or none) that the following procedures are likely to have on both bias and sampling error in an experimental study.
A) Assigning treatments to subjects alphabetically, not randomly
B) Increasing sample size
C) Calculating power
D) Applying every treatment to every experimental unit in random order
E) Using a sample of convenience instead of a random sample
F) Testing only one treatment group, without a control group
G) Using a balanced design
H) Informing the human subjects which treatment they will receive

The main ones I'm struggling with are C,D, and E (especially C, because I would think it would depend on what the power was one you calculated it).

I pretty sure I have A,B, and F-H figured out, but I may be wrong. I'm thinking that A, F, and H will increase bias but have no effect on sampling error, whereas B and G will reduce sampling error but not affect bias.

A) Increase bias, no effect on sampling error

B) Decrease sampling error, no effect on bias
C) Depends on the power calculated
D) No effect on bias or sampling error
E) Increase bias, decrease sampling error
F) Increase bias, no effect on sampling error
G) Decrease sampling error, no effect on bias
H) Increase bias, no effect on sampling error

To address your question, let's first explain what bias and sampling error mean in the context of an experimental study:

- Bias refers to any systematic error or distortion in a study that leads to results that are consistently different from the true value. It can be caused by various factors, such as the study design, selection of participants, or measurement methods.

- Sampling error, on the other hand, is the natural variation that occurs when a sample is chosen from a larger population. It represents the discrepancy between the sample estimate and the true value in the entire population.

Now, let's analyze the consequences of the given procedures on bias and sampling error:

A) Assigning treatments to subjects alphabetically, not randomly:
This procedure is likely to increase bias because it introduces a systematic pattern in the assignment of treatments. For example, if treatments are assigned alphabetically by last name, it may inadvertently group individuals with similar characteristics together. However, it may have no effect on sampling error since the sample is still representative of the population.

B) Increasing sample size:
Increasing the sample size generally reduces sampling error. By including more participants, the estimates derived from the sample are likely to be closer to the true population values. However, it does not directly affect bias unless there are differential characteristics between the larger and smaller samples.

C) Calculating power:
Calculating power refers to assessing the likelihood of detecting a true effect if it exists. It does not directly affect bias or sampling error. Power calculations help researchers determine an appropriate sample size or design, which indirectly impacts sampling error. If the power is adequately calculated, it can reduce the risk of a Type II error (failing to detect a true effect).

D) Applying every treatment to every experimental unit in random order:
This procedure is likely to have no effect on bias since it randomly assigns treatments and eliminates systematic patterns. However, it may reduce sampling error because it ensures that each treatment is equally represented across the experimental units, allowing a more precise estimate of treatment effects.

E) Using a sample of convenience instead of a random sample:
Using a convenience sample, which is not representative of the population, can increase both bias and sampling error. Bias may arise from the non-random selection process, leading to a sample that does not accurately represent the population. Sampling error can be higher because the sample may not capture the full range of variation present in the population.

F) Testing only one treatment group, without a control group:
This procedure can increase bias because it lacks a baseline for comparison. Without a control group, it becomes difficult to determine the true effect of the treatment. However, it may not directly affect sampling error unless there are other confounding factors.

G) Using a balanced design:
A balanced design involves allocating treatments equally across experimental units. It helps control for potential bias by ensuring that each treatment group is adequately represented. However, it does not directly reduce sampling error. By increasing the balance and control in the design, bias is decreased, but sampling error depends on other factors like the sample size and representativeness.

H) Informing the human subjects which treatment they will receive:
Informing subjects of their treatment can introduce bias if they alter their behavior or responses based on that information. This bias is known as the placebo effect or the Hawthorne effect. It can lead to the overestimation or underestimation of treatment effects. However, it may not directly affect sampling error unless there are differential effects between treatment groups.

In summary, your understanding is mostly correct. Assigning treatments alphabetically (A), testing only one treatment group without a control group (F), and informing subjects of their treatment (H) can increase bias but have no direct effect on sampling error. Increasing sample size (B) and using a balanced design (G) can reduce sampling error but do not directly affect bias. Calculating power (C) does not directly affect either bias or sampling error. Using a sample of convenience (E) can increase both bias and sampling error. Finally, applying every treatment to every experimental unit in random order (D) has no direct effect on bias but can reduce sampling error.

Let's go through each of the procedures and their consequences on bias and sampling error in an experimental study:

A) Assigning treatments to subjects alphabetically, not randomly:
This procedure is likely to increase bias because it introduces a systematic order in assigning treatments, which may result in certain characteristics or factors being associated with the treatment assignments. However, it may not have a direct effect on sampling error since the sample itself remains the same.

B) Increasing sample size:
Increasing the sample size is likely to decrease sampling error. A larger sample size provides more representative data, reducing the impact of random variation and increasing the precision of estimates. It may not directly affect bias, as bias is related to the way the study is conducted rather than the size of the sample itself.

C) Calculating power:
Calculating power is a way to determine the probability of detecting an effect in a study, given a specific sample size and effect size. It does not directly affect bias since it represents the study's ability to identify true effects. However, increasing power by choosing larger sample sizes or greater effect sizes can reduce the risk of Type II errors (false negatives) and improve the reliability of the study's findings.

D) Applying every treatment to every experimental unit in random order:
This procedure, known as a completely randomized design, can help reduce bias by ensuring that each experimental unit has an equal chance of receiving any treatment. By randomly assigning treatments, it helps control for potential confounding factors. It may not directly affect sampling error, as the sample size and variability remain unchanged.

E) Using a sample of convenience instead of a random sample:
Using a sample of convenience, such as selecting subjects who are readily available, can introduce bias. The sample may not be representative of the population of interest, leading to a biased estimate of treatment effects. It may not directly influence sampling error as it is related to the representativeness of the sample rather than the precision of estimates.

F) Testing only one treatment group, without a control group:
Testing only one treatment group without a control group can introduce bias. Without a control group, it becomes challenging to measure the true effect of the treatment since there is no basis for comparison. This can lead to overestimating the treatment's effectiveness. It may not directly affect sampling error, as it is related to the estimation of treatment effects rather than the variability of estimates.

G) Using a balanced design:
Using a balanced design ensures that each treatment has an equal number of subjects, which helps control for potential confounding factors. This can help reduce bias by distributing the impact of those factors evenly among the treatment groups. It may not directly affect sampling error, as the sample size and variability remain the same.

H) Informing the human subjects which treatment they will receive:
Informing human subjects about the treatment they will receive can introduce bias. Knowledge of the treatment can influence their behavior, perceptions, or responses, leading to biased results. It may not directly affect sampling error, as it is related to the estimation of treatment effects rather than the variability of estimates.

To summarize, the consequences of the procedures on bias and sampling error are as follows:
- A, E, F, and H increase bias.
- B and G reduce sampling error.
- C does not directly affect bias but can affect the study's ability to detect true effects.
- D does not directly affect bias or sampling error.

Please note that these are general consequences, and the impact of each procedure may vary depending on the specific circumstances of the study.