551). The difference in purposes of evaluating the quality of studies in quantitative and quantitative research is one of the reasons that the concept of reliability is irrelevant in qualitative research. According to Stenbacka, (2001) “the concept of reliability is even misleading in qualitative research.

The most widely used criteria for evaluating qualitative content analysis are those developed by Lincoln and Guba (1985). They used the term trustworthiness. The aim of trustworthiness in a qualitative inquiry is to support the argument that the inquiry’s findings are “worth paying attention to” (Lincoln & Guba, 1985).

In essence, a more rigorous research process will result in more trustworthy findings. A number of features are thought to define rigorous qualitative research: transparency, maximal validity or credibility, maximal reliability or dependability, comparativeness, and reflexivity.

Selection bias occurs when the presence of observations in the sample depends on the value of the variable of interest. When this happens, the sample is no longer randomly drawn from the population being studied, and any inferences about that population that are based on the selected sample will be biased.

In research, bias occurs when “systematic error [is] introduced into sampling or testing by selecting or encouraging one outcome or answer over others” 7. Bias can occur at any phase of research, including study design or data collection, as well as in the process of data analysis and publication (Figure 1).

If you notice the following, the source may be biased: Heavily opinionated or one-sided. Relies on unsupported or unsubstantiated claims. Presents highly selected facts that lean to a certain outcome.

Selection bias is a distortion in a measure of association (such as a risk ratio) due to a sample selection that does not accurately reflect the target population. This biases the study when the association between a risk factor and a health outcome differs in dropouts compared with study participants.

Bias is a tendency to lean in a certain direction, either in favor of or against a particular thing. To be truly biased means to lack a neutral viewpoint on a particular topic. If you’re biased toward something, then you lean favorably toward it; you tend to think positively of it.

Bias is direct current ( DC ) deliberately made to flow, or DC voltage deliberately applied, between two points for the purpose of controlling a circuit . In a bipolar transistor , the bias is usually specified as the direction in which DC from a battery or power supply flows between the emitter and the base.

Bias is like the intercept added in a linear equation. It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. Thus, Bias is a constant which helps the model in a way that it can fit best for the given data.

Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1.

bias is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).” Bias is the accuracy of our predictions. A high bias means the prediction will be inaccurate.

Each neuron except for in the input-layer has a bias.

Originally a perceptron was only referring to neural networks with a step function as the transfer function. In that case of course the difference is that the logistic regression uses a logistic function and the perceptron uses a step function.

A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor. You need to do layer sharing. You want non-linear topology (e.g. a residual connection, a multi-branch model)

Sequential is the easiest way to build a model in Keras. It allows you to build a model layer by layer. Each layer has weights that correspond to the layer the follows it. We use the ‘add()’ function to add layers to our model. We will add two layers and an output layer.

The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. In this tutorial, you will discover how to use the more flexible functional API in Keras to define deep learning models.